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Wolf Hybrids
Wolf Hybrids By Claudine Wilkins and Jessica Rock, Founders of Animal Law Source™ DEFINITION By definition, the wolf-dog hybrid is a cross between a domestic dog (Canis familiaris) and a wild Wolf (Canis Lupus). Wolves are the evolutionary ancestor of dogs. Dogs evolved from wolves through thousands of years of adaptation, living and being selectively bred and domesticated by humans. Because dogs and wolves are evolutionarily connected, dogs and wolves can breed together. Although this cross breeding can occur naturally, it is a rare occurrence in the wild due to the territorial and aggressive nature of wolves. Recently, the breeding of a dog with a wolf has become an accepted new phenomenon because wolf-hybrids are considered to be exotic and prestigious to own. To circumvent the prohibition against keeping wolves as pets, enterprising people have gone underground and are breeding and selling wolf-dog hybrids in their backyards. Consequently, an increase in the number of hybrids are being possessed without the minimum public safeguards required for the common domestic dog. TRAITS OF DOGS AND WOLVES Since wolf hybrids are a genetic mixture of wolves and dogs, they can seem to be similar on the surface. However, even though both may appear to be physically similar, there are many behavioral differences between wolves and dogs. Wolves raised in the wild appear to fear humans and will avoid contact whenever possible. Wolves raised in captivity are not as fearful of humans. This suggests that such fear may be learned rather than inherited. Dogs, on the other hand, socialize quite readily with humans, often preferring human company to that of other dogs. -
Swarms and Swarm Intelligence
SOFTWARE TECHNOLOGIES homogeneous. Intelligent swarms can also comprise heterogeneous elements Swarms from the outset, reflecting different capabilities as well as a possible social structure. and Swarm Researchers have used agent swarms as a computer modeling tech- nique and as a tool to study complex systems. Simulation examples include Intelligence bird swarms and business, economics, and ecological systems. In swarm sim- Michael G. Hinchey, Loyola College in Maryland ulations, each agent tries to maximize Roy Sterritt, University of Ulster its given parameters. Chris Rouff, Lockheed Martin Advanced Technology Laboratories In terms of bird swarms, each bird tries to find another to fly with, and then flies slightly higher to one side to reduce drag, with the birds eventually forming a flock. Other types of swarm simulations exhibit unlikely emergent behaviors, which are sums of simple Intelligent swarm technologies solve individual behaviors that form com- complex problems that traditional plex and often unexpected behaviors approaches cannot. when aggregated. Swarm intelligence Gerardo Beni and Jing Wang intro- duced the term swarm intelligence in a 1989 article (“Swarm Intelligence,” Proc. 7th Ann. Meeting of the Robotics e are all familiar with working together to achieve a goal Society of Japan, RSJ Press, 1989, pp. swarms in nature. The and produce significant results. 425-428). Swarm intelligence tech- word swarm conjures Swarms may operate on or under the niques (note the difference from intel- up images of large Earth’s surface, under water, or on ligent swarms) are population-based W groups of small insects other planets. stochastic methods used in combina- in which each member performs a torial optimization problems in which simple role, but the action produces SWARMS AND INTELLIGENCE the collective behavior of relatively sim- complex behavior as a whole. -
Wolf Interactions with Non-Prey
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln USGS Northern Prairie Wildlife Research Center US Geological Survey 2003 Wolf Interactions with Non-prey Warren B. Ballard Texas Tech University Ludwig N. Carbyn Canadian Wildlife Service Douglas W. Smith US Park Service Follow this and additional works at: https://digitalcommons.unl.edu/usgsnpwrc Part of the Animal Sciences Commons, Behavior and Ethology Commons, Biodiversity Commons, Environmental Policy Commons, Recreation, Parks and Tourism Administration Commons, and the Terrestrial and Aquatic Ecology Commons Ballard, Warren B.; Carbyn, Ludwig N.; and Smith, Douglas W., "Wolf Interactions with Non-prey" (2003). USGS Northern Prairie Wildlife Research Center. 325. https://digitalcommons.unl.edu/usgsnpwrc/325 This Article is brought to you for free and open access by the US Geological Survey at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in USGS Northern Prairie Wildlife Research Center by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. 10 Wolf Interactions with Non-prey Warren B. Ballard, Ludwig N. Carbyn, and Douglas W. Smith WOLVES SHARE THEIR ENVIRONMENT with many an wolves and non-prey species. The inherent genetic, be imals besides those that they prey on, and the nature of havioral, and morphological flexibility of wolves has the interactions between wolves and these other crea allowed them to adapt to a wide range of habitats and tures varies considerably. Some of these sympatric ani environmental conditions in Europe, Asia, and North mals are fellow canids such as foxes, coyotes, and jackals. America. Therefore, the role of wolves varies consider Others are large carnivores such as bears and cougars. -
Glimpse of an African… Wolf? Cécile Bloch
$6.95 Glimpse of an African… Wolf ? PAGE 4 Saving the Red Wolf Through Partnerships PAGE 9 Are Gray Wolves Still Endangered? PAGE 14 Make Your Home Howl Members Save 10% Order today at shop.wolf.org or call 1-800-ELY-WOLF Your purchases help support the mission of the International Wolf Center. VOLUME 25, NO. 1 THE QUARTERLY PUBLICATION OF THE INTERNATIONAL WOLF CENTER SPRING 2015 4 Cécile Bloch 9 Jeremy Hooper 14 Don Gossett In the Long Shadow of The Red Wolf Species Survival Are Gray Wolves Still the Pyramids and Beyond: Plan: Saving the Red Wolf Endangered? Glimpse of an African…Wolf? Through Partnerships In December a federal judge ruled Geneticists have found that some In 1967 the number of red wolves that protections be reinstated for of Africa’s golden jackals are was rapidly declining, forcing those gray wolves in the Great Lakes members of the gray wolf lineage. remaining to breed with the more wolf population area, reversing Biologists are now asking: how abundant coyote or not to breed at all. the USFWS’s 2011 delisting many golden jackals across Africa The rate of hybridization between the decision that allowed states to are a subspecies known as the two species left little time to prevent manage wolves and implement African wolf? Are Africa’s golden red wolf genes from being completely harvest programs for recreational jackals, in fact, wolves? absorbed into the expanding coyote purposes. If biological security is population. The Red Wolf Recovery by Cheryl Lyn Dybas apparently not enough rationale for Program, working with many other conservation of the species, then the organizations, has created awareness challenge arises to properly express and laid a foundation for the future to the ecological value of the species. -
Oregon Wolf Conservation and Management 2020 Annual Report
Oregon Wolf Conservation and Management 2020 Annual Report This report to the Oregon Fish and Wildlife Commission presents information on the status, distribution, and management of wolves in the State of Oregon from January 1, 2020 to December 31, 2020. Suggested Citation: Oregon Department of Fish and Wildlife. 2021. Oregon Wolf Conservation and Management 2020 Annual Report. Oregon Department of Fish and Wildlife, 4034 Fairview Industrial Drive SE. Salem, OR, 97302 TABLE OF CONTENTS EXECUTIVE SUMMARY ...................................................................................................................... 2 OREGON WOLF PROGRAM OVERVIEW .......................................................................................... 3 Regulatory Status .................................................................................................................................. 3 Minimum Numbers, Reproduction, and Distribution ........................................................................... 4 Monitoring ............................................................................................................................................ 6 Information and Outreach ..................................................................................................................... 7 Wolf Program Funding ......................................................................................................................... 8 LIVESTOCK DEPREDATION MANAGEMENT ................................................................................ -
Ecology of the European Badger (Meles Meles) in the Western Carpathian Mountains: a Review
Wildl. Biol. Pract., 2016 Aug 12(3): 36-50 doi:10.2461/wbp.2016.eb.4 REVIEW Ecology of the European Badger (Meles meles) in the Western Carpathian Mountains: A Review R.W. Mysłajek1,*, S. Nowak2, A. Rożen3, K. Kurek2, M. Figura2 & B. Jędrzejewska4 1 Institute of Genetics and Biotechnology, Faculty of Biology, University of Warsaw, Pawińskiego 5a, 02-106 Warszawa, Poland. 2 Association for Nature “Wolf”, Twardorzeczka 229, 34-324 Lipowa, Poland. 3 Institute of Environmental Sciences, Jagiellonian University, Gronostajowa 7, 30-387 Kraków, Poland. 4 Mammal Research Institute, Polish Academy of Sciences, Waszkiewicza 1c, 17-230 Białowieża, Poland. * Corresponding author email: [email protected]. Keywords Abstract Altitudinal Gradient; This article summarizes the results of studies on the ecology of the European Diet Composition; badger (Meles meles) conducted in the Western Carpathians (S Poland) Meles meles; from 2002 to 2010. Badgers inhabiting the Carpathians use excavated setts Mustelidae; (53%), caves and rock crevices (43%), and burrows under human-made Sett Utilization; constructions (4%) as permanent shelters. Excavated setts are located up Spatial Organization. to 640 m a.s.l., but shelters in caves and crevices can be found as high as 1,050 m a.s.l. Badger setts are mostly located on slopes with southern, eastern or western exposure. Within their territories, ranging from 3.35 to 8.45 km2 (MCP100%), badgers may possess 1-12 setts. Family groups are small (mean = 2.3 badgers), population density is low (2.2 badgers/10 km2), as is reproduction (0.57 young/year/10 km2). Hunting by humans is the main mortality factor (0.37 badger/year/10 km2). -
Prey Preference and Dietary Overlap of Sympatric Snow Leopard and Tibetan Wolf in Central Part of Wangchuck Centennial National Park
Prey Preference and Dietary overlap of Sympatric Snow leopard and Tibetan Wolf in Central Part of Wangchuck Centennial National Park Yonten Jamtsho Wangchuck Centennial National Park Department of Forest and Park Services Ministry of Agriculture and Forest 2017 Abstract Snow leopards have been reported to kill livestock in most parts of their range but the extent of this predation and its impact on local herders is poorly understood. There has been even no effort in looking at predator-prey relationships and often we make estimates of prey needs based on studies from neighboring regions. Therefore this study is aimed at analysing livestock depredation, diets of snow leopard and Tibetan wolf and its implication to herder’s livelihood in Choekhortoe and Dhur region of Wangchuck Cetennial National Park. Data on the livestock population, frequency of depredation, and income lost were collected from a total of 38 respondents following census techniques. In addition scats were analysed to determine diet composition and prey preferences. The results showed 38 herders rearing 2815 heads of stock with average herd size of 74.07 stocks with decreasing trend over the years due to depredation. As a result Choekhortoe lost 8.6% while Dhur lost 5.07% of total annual income. Dietary analysis showed overlap between two species indicated by Pianka index value of 0.83 for Dhur and 0.96 for Choekhortoe. The prey preference for snow leopard and Tibetan wolf are domestic sheep and blue sheep respectively, where domestic sheep is an income for herders and blue sheep is important for conservation of snow leopard. -
Early History of the Wolf, Black Bear, and Mountain Lion in Arkansas Annalea K
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by ScholarWorks@UARK Journal of the Arkansas Academy of Science Volume 55 Article 4 2001 Early History of the Wolf, Black Bear, and Mountain Lion in Arkansas Annalea K. Bowers University of Arkansas at Little Rock Leah D. Lucio University of Arkansas at Little Rock David W. Clark University of Arkansas at Little Rock Susan P. Rakow University of Arkansas at Little Rock Gary A. Heidt University of Arkansas at Little Rock, [email protected] Follow this and additional works at: http://scholarworks.uark.edu/jaas Part of the Zoology Commons Recommended Citation Bowers, Annalea K.; Lucio, Leah D.; Clark, David W.; Rakow, Susan P.; and Heidt, Gary A. (2001) "Early History of the Wolf, Black Bear, and Mountain Lion in Arkansas," Journal of the Arkansas Academy of Science: Vol. 55 , Article 4. Available at: http://scholarworks.uark.edu/jaas/vol55/iss1/4 This article is available for use under the Creative Commons license: Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0). Users are able to read, download, copy, print, distribute, search, link to the full texts of these articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. This Article is brought to you for free and open access by ScholarWorks@UARK. It has been accepted for inclusion in Journal of the Arkansas Academy of Science by an authorized editor of ScholarWorks@UARK. For more information, please contact [email protected]. Journal of the Arkansas Academy of Science, Vol. -
An Exploration in Stigmergy-Based Navigation Algorithms
From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms عمر بهر تيری محبت ميری خدمت گر رہی ميں تری خدمت کےقابل جب هوا توچل بسی )اقبال( To my late parents with love and eternal appreciation, whom I lost during my PhD studies Örebro Studies in Technology 79 ALI ABDUL KHALIQ From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms © Ali Abdul Khaliq, 2018 Title: From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms Publisher: Örebro University 2018 www.publications.oru.se Print: Örebro University, Repro 05/2018 ISSN 1650-8580 ISBN 978-91-7529-253-3 Abstract Ali Abdul Khaliq (2018): From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms. Örebro Studies in Technology 79. Navigation is a core functionality of mobile robots. To navigate autonomously, a mobile robot typically relies on internal maps, self-localization, and path plan- ning. Reliable navigation usually comes at the cost of expensive sensors and often requires significant computational overhead. Many insects in nature perform robust, close-to-optimal goal directed naviga- tion without having the luxury of sophisticated sensors, powerful computational resources, or even an internally stored map. They do so by exploiting a simple but powerful principle called stigmergy: they use their environment as an external memory to store, read and share information. In this thesis, we explore the use of stigmergy as an alternative route to realize autonomous navigation in practical robotic systems. In our approach, we realize a stigmergic medium using RFID (Radio Frequency Identification) technology by embedding a grid of read-write RFID tags in the floor. -
Flocks, Herds, and Schools: a Distributed Behavioral Model 1
Published in Computer Graphics, 21(4), July 1987, pp. 25-34. (ACM SIGGRAPH '87 Conference Proceedings, Anaheim, California, July 1987.) Flocks, Herds, and Schools: A Distributed Behavioral Model 1 Craig W. Reynolds Symbolics Graphics Division [obsolete addresses removed 2] Abstract The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the natural world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle system, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds. Categories and Subject Descriptors: 1.2.10 [Artificial Intelligence]: Vision and Scene Understanding; 1.3.5 [Computer Graphics]: Computational Geometry and Object Modeling; 1.3.7 [Computer Graphics]: Three Dimensional Graphics and Realism-Animation: 1.6.3 [Simulation and Modeling]: Applications. General Terms: Algorithms, design.b Additional Key Words, and Phrases: flock, herd, school, bird, fish, aggregate motion, particle system, actor, flight, behavioral animation, constraints, path planning. -
HV Battery SOC Estimation Algorithm Based on PSO
HV Battery SOC Estimation Algorithm Based on PSO Qiang Sun1, Haiying Lv1, Shasha Wang2, Shixiang Jing1, Jijun Chen1 and Rui Fang1 1College of Engineering and Technology, Tianjin Agricultural University, Jinjing Road, Tianjin, China 2Beijing Electric Vehicle Co., Ltd., East Ring Road, Beijing, China [email protected], [email protected] Keywords: HV battery, SOC, PSO, individual optimum, global optimum. Abstract: In view of the traditional algorithm to estimate the electric vehicle battery state of charge (SOC), lots of problems were caused by the inaccurate battery model parameter estimation error. Particle swarm optimization (PSO) algorithm is a global optimization algorithm based on swarm intelligence, it has intrinsic parallel search mechanism, and is suitable for the complex optimization field. PSO is simple in concept with few using of parameters, and easily in implementation. It is proved to be an efficient method to solve optimization problems, and has successfully been applied in area of function optimization. This article adopts the PSO optimization method to solve the problem of battery parameters. The paper make a new attempt on SOC estimation method, applying PSO to SOC parameters optimization. It can build optimization model to get a more accurate estimate SOC in the case of less parameters with faster convergence speed. 1 INTRODUCTION suitable for the complex optimization field. The paper make a new attempt on SOC estimation Lithium battery has many advantages such as high method, applying PSO to SOC parameters energy density, high power density, long cycle life optimization, It can build optimization model to get and so on. As the energy storage element of electric a more accurate estimate SOC in the case of less vehicles, it is used widely. -
Mathematical Modelling and Applications of Particle Swarm Optimization
Master’s Thesis Mathematical Modelling and Simulation Thesis no: 2010:8 Mathematical Modelling and Applications of Particle Swarm Optimization by Satyobroto Talukder Submitted to the School of Engineering at Blekinge Institute of Technology In partial fulfillment of the requirements for the degree of Master of Science February 2011 Contact Information: Author: Satyobroto Talukder E-mail: [email protected] University advisor: Prof. Elisabeth Rakus-Andersson Department of Mathematics and Science, BTH E-mail: [email protected] Phone: +46455385408 Co-supervisor: Efraim Laksman, BTH E-mail: [email protected] Phone: +46455385684 School of Engineering Internet : www.bth.se/com Blekinge Institute of Technology Phone : +46 455 38 50 00 SE – 371 79 Karlskrona Fax : +46 455 38 50 57 Sweden ii ABSTRACT Optimization is a mathematical technique that concerns the finding of maxima or minima of functions in some feasible region. There is no business or industry which is not involved in solving optimization problems. A variety of optimization techniques compete for the best solution. Particle Swarm Optimization (PSO) is a relatively new, modern, and powerful method of optimization that has been empirically shown to perform well on many of these optimization problems. It is widely used to find the global optimum solution in a complex search space. This thesis aims at providing a review and discussion of the most established results on PSO algorithm as well as exposing the most active research topics that can give initiative for future work and help the practitioner improve better result with little effort. This paper introduces a theoretical idea and detailed explanation of the PSO algorithm, the advantages and disadvantages, the effects and judicious selection of the various parameters.