The Role of Swarming Sites for Maintaining Gene Flow in the Brown
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
A New Species of Long-Eared Bat (Plecotus; Vespertilionidae, Mammalia) from Ethiopia
A new species of long-eared bat (Plecotus; Vespertilionidae, Mammalia) from Ethiopia Sergey V. Kruskop & Leonid A. Lavrenchenko Abstract. A new species of Plecotus is described, based on several specimens from southern Ethiopia, the southernmost distribution record of the genus. The new species differs from all known species of Plecotus in size, cranial proportions and pelage coloration. In some metric and qualitative traits (skull size and face shape) it resembles P. auritus. The similarities between these two species may be convergent, though. The shape of the baculum of the new species is strikingly similar to that of the insular P. teneriffae. At present the phylogenetic relationships among the species of Plecotus remain unresolved. Key words: Plecotus, new species, taxonomy, systematics, craniometry, Ethiopia. Introduction Plecotine bats are a rather small group within the family Vespertilionidae. Never- theless, their taxonomy is unsettled and therefore has been the subject of several revisions (Fedyk & Ruprecht 1983, Frost & Timm 1992). The genus Plecotus E. Geoffroy, 1818 s. str. includes two to four currently recognized species but also many named taxa of uncertain rank (Yoshiyuki 1991). Most of these taxa are pres- ently included in the polymorphous species P. austriacus (Fischer, 1829) which is widely distributed from Algeria and Central Europe to the Arabian peninsula and the Himalayas (Strelkov 1988). The characters in which it differs from the also widely distributed but more monomorphic P. auritus (Linnaeus, 1758) were de- scribed by Strelkov (1988). Plecotus austriacus was the only member of the genus known to occur in Africa (Corbet 1978), and the Ethiopian highlands were reported as the southernmost part of its distribution area there (Yalden et al. -
Inference of Causal Information Flow in Collective Animal Behavior Warren M
IEEE TMBMC SPECIAL ISSUE 1 Inference of Causal Information Flow in Collective Animal Behavior Warren M. Lord, Jie Sun, Nicholas T. Ouellette, and Erik M. Bollt Abstract—Understanding and even defining what constitutes study how the microscopic interactions scale up to give rise animal interactions remains a challenging problem. Correlational to the macroscopic properties [26]. tools may be inappropriate for detecting communication be- The third of these goals—how the microscopic individual- tween a set of many agents exhibiting nonlinear behavior. A different approach is to define coordinated motions in terms of to-individual interactions determine the macroscopic group an information theoretic channel of direct causal information behavior—has arguably received the most scientific attention flow. In this work, we present an application of the optimal to date, due to the availability of simple models of collective causation entropy (oCSE) principle to identify such channels behavior that are easy to simulate on computers, such as the between insects engaged in a type of collective motion called classic Reynolds [27], Vicsek [26], and Couzin [28] models. swarming. The oCSE algorithm infers channels of direct causal inference between insects from time series describing spatial From these kinds of studies, a significant amount is known movements. The time series are discovered by an experimental about the nature of the emergence of macroscopic patterns protocol of optical tracking. The collection of channels infered and ordering in active, collective systems [29]. But in argu- by oCSE describes a network of information flow within the ing that such simple models accurately describe real animal swarm. We find that information channels with a long spatial behavior, one must implicitly make the assumption that the range are more common than expected under the assumption that causal information flows should be spatially localized. -
Swarm Intelligence
Swarm Intelligence Leen-Kiat Soh Computer Science & Engineering University of Nebraska Lincoln, NE 68588-0115 [email protected] http://www.cse.unl.edu/agents Introduction • Swarm intelligence was originally used in the context of cellular robotic systems to describe the self-organization of simple mechanical agents through nearest-neighbor interaction • It was later extended to include “any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies and other animal societies” • This includes the behaviors of certain ants, honeybees, wasps, cockroaches, beetles, caterpillars, and termites Introduction 2 • Many aspects of the collective activities of social insects, such as ants, are self-organizing • Complex group behavior emerges from the interactions of individuals who exhibit simple behaviors by themselves: finding food and building a nest • Self-organization come about from interactions based entirely on local information • Local decisions, global coherence • Emergent behaviors, self-organization Videos • https://www.youtube.com/watch?v=dDsmbwOrHJs • https://www.youtube.com/watch?v=QbUPfMXXQIY • https://www.youtube.com/watch?v=M028vafB0l8 Why Not Centralized Approach? • Requires that each agent interacts with every other agent • Do not possess (environmental) obstacle avoidance capabilities • Lead to irregular fragmentation and/or collapse • Unbounded (externally predetermined) forces are used for collision avoidance • Do not possess distributed tracking (or migration) -
Intra- and Interspecific Competition in Western Barbastelle Bats
Intra- and interspecific competition in western barbastelle bats (Bbastell bastellus, SCHREBER 1774): Niche differentiation in a specialised bat species, revealed via radio-tracking. Dissertation zur Erlangung des Grades "Doktor der Naturwissenschaften" am Fachbereich Biologie der Johannes Gutenberg-Universität in Mainz Jessica Hillen geb. am 09.01.1981 in Zell (Mosel) Mainz, 2011 Tag der mündlichen Prüfung: 16.12.2011 Western barbastelle bats in their tree roost. Background: View of the brook valley 'Ahringsbachtal'. Contents Contents. Abstract..............................................................................................................................................5 General introduction. ..........................................................................................................................7 Chapter I. Spatial organisation and foraging site fidelity of a population of female western barbastelle bats...................................................................................................................................................12 Abstract....................................................................................................................................13 1. Introduction. ....................................................................................................................14 2. Materials and methods......................................................................................................16 3. Results. ............................................................................................................................21 -
Adaptive Exploration of a Uavs Swarm for Distributed Targets Detection and Tracking
Adaptive Exploration of a UAVs Swarm for Distributed Targets Detection and Tracking Mario G. C. A. Cimino1, Massimiliano Lega2, Manilo Monaco1 and Gigliola Vaglini1 1Department of Information Engineering, University of Pisa, 56122 Pisa, Italy 2Department of Engineering University of Naples “Parthenope”, 80143 Naples, Italy Keywords: UAV, Swarm Intelligence, Stigmergy, Flocking, Differential Evolution, Target Detection, Target Tracking. Abstract: This paper focuses on the problem of coordinating multiple UAVs for distributed targets detection and tracking, in different technological and environmental settings. The proposed approach is founded on the concept of swarm behavior in multi-agent systems, i.e., a self-formed and self-coordinated team of UAVs which adapts itself to mission-specific environmental layouts. The swarm formation and coordination are inspired by biological mechanisms of flocking and stigmergy, respectively. These mechanisms, suitably combined, make it possible to strike the right balance between global search (exploration) and local search (exploitation) in the environment. The swarm adaptation is based on an evolutionary algorithm with the objective of maximizing the number of tracked targets during a mission or minimizing the time for target discovery. A simulation testbed has been developed and publicly released, on the basis of commercially available UAVs technology and real-world scenarios. Experimental results show that the proposed approach extends and sensibly outperforms a similar approach in the literature. 1 INTRODUCTION exploration of UAVs swarms are not sufficiently mature: limited flexibility, complex management and In this paper we consider the problem of discovering application-dependent design are the main issues to and tracking static or dynamic targets in unstructured solve (Senanayake et al. -
An Introduction to Swarm Intelligence Issues
An Introduction to Swarm Intelligence Issues Gianni Di Caro [email protected] IDSIA, USI/SUPSI, Lugano (CH) 1 Topics that will be discussed Basic ideas behind the notion of Swarm Intelligence The role of Nature as source of examples and ideas to design new algorithms and multi-agent systems From observations to models and to algorithms Self-organized collective behaviors The role of space and communication to obtain self-organization Social communication and stigmergic communication Main algorithmic frameworks based on the notion of Swarm Intelligence: Collective Intelligence, Particle Swarm Optimization, Ant Colony Optimization Computational complexity, NP-hardness and the need of (meta)heuristics Some popular metaheuristics for combinatorial optimization tasks 2 Swarm Intelligence: what’s this? Swarm Intelligence indicates a recent computational and behavioral metaphor for solving distributed problems that originally took its inspiration from the biological examples provided by social insects (ants, termites, bees, wasps) and by swarming, flocking, herding behaviors in vertebrates. Any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insects and other animal societies. [Bonabeau, Dorigo and Theraulaz, 1999] . however, we don’t really need to “stick” on examples from Nature, whose constraints and targets might differ profoundly from those of our environments of interest . 3 Where does it come from? Nest building in termite or honeybee societies Foraging in ant colonies Fish schooling Bird flocking . 4 Nature’s examples of SI Fish schooling ( c CORO, CalTech) 5 Nature’s examples of SI (2) Birds flocking in V-formation ( c CORO, Caltech) 6 Nature’s examples of SI (3) Termites’ nest ( c Masson) 7 Nature’s examples of SI (4) Bees’ comb ( c S. -
Expert Assessment of Stigmergy: a Report for the Department of National Defence
Expert Assessment of Stigmergy: A Report for the Department of National Defence Contract No. W7714-040899/003/SV File No. 011 sv.W7714-040899 Client Reference No.: W7714-4-0899 Requisition No. W7714-040899 Contact Info. Tony White Associate Professor School of Computer Science Room 5302 Herzberg Building Carleton University 1125 Colonel By Drive Ottawa, Ontario K1S 5B6 (Office) 613-520-2600 x2208 (Cell) 613-612-2708 [email protected] http://www.scs.carleton.ca/~arpwhite Expert Assessment of Stigmergy Abstract This report describes the current state of research in the area known as Swarm Intelligence. Swarm Intelligence relies upon stigmergic principles in order to solve complex problems using only simple agents. Swarm Intelligence has been receiving increasing attention over the last 10 years as a result of the acknowledgement of the success of social insect systems in solving complex problems without the need for central control or global information. In swarm- based problem solving, a solution emerges as a result of the collective action of the members of the swarm, often using principles of communication known as stigmergy. The individual behaviours of swarm members do not indicate the nature of the emergent collective behaviour and the solution process is generally very robust to the loss of individual swarm members. This report describes the general principles for swarm-based problem solving, the way in which stigmergy is employed, and presents a number of high level algorithms that have proven utility in solving hard optimization and control problems. Useful tools for the modelling and investigation of swarm-based systems are then briefly described. -
Conservation Assessments for Five Forest Bat Species in the Eastern United States
United States Department of Agriculture Conservation Forest Service Assessments for Five General Technical Report NC-260 Technical Guide Forest Bat Species in the 2006 Eastern United States Front Cover: Illustrations by Fiona Reid, Ontario, Canada ©. Species from top: Pipistrellus subflavus, Myotis leibii, Myotis austroriparius, Myotis septentrionalis, Nycticeius humeralis. United States Department of Agriculture Conservation Forest Service Assessments for Five General Technical Report NC-260 Technical Guide Forest Bat Species in the 2006 Eastern United States Edited by Frank R. Thompson, III Thompson, Frank R., III, ed. 2006. Conservation assessments for five forest bat species in the Eastern United States. Gen. Tech. Rep. NC-260. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Research Station. 82 p. Assesses the status, distribution, conservation, and management considerations for five Regional Forester Sensitive Species of forest bats on national forests in the Eastern United States: eastern pipistrelle, evening bat, southeastern myotis, eastern small-footed myotis, and northern long-eared bat. Includes information on the taxonomy, description, life history, habitat distribution, status, and population biology of each species. KEY WORDS: conservation status, habitat use, life history, Myotis austroriparius (southeastern myotis), Myotis leibii (eastern small-footed myotis), Myotis septentrionalis (northern long-eared bat), Pipistrellus subflavus (eastern pipistrelle), Nycticeius humeralis (evening bat), Region 9, USDA Forest Service Disclaimer The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, familial status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because all or part of an individual’s income is derived from any public assistance program. -
A Contribution to the Bats Inhabiting Arid Steppe Habitats in Central Mongolia Nyambayar Batbayar Mongolian Academy of Sciences, [email protected]
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Erforschung biologischer Ressourcen der Mongolei Institut für Biologie der Martin-Luther-Universität / Exploration into the Biological Resources of Halle-Wittenberg Mongolia, ISSN 0440-1298 2010 A Contribution to the Bats Inhabiting Arid Steppe Habitats in Central Mongolia Nyambayar Batbayar Mongolian Academy of Sciences, [email protected] Ariunbold Jargalsaikhan Mongolian State University of Education, [email protected] Sukhchuluun Gansukh Mongolian Academy of Sciences, [email protected] Follow this and additional works at: http://digitalcommons.unl.edu/biolmongol Part of the Asian Studies Commons, Biodiversity Commons, Environmental Sciences Commons, Nature and Society Relations Commons, and the Other Animal Sciences Commons Batbayar, Nyambayar; Jargalsaikhan, Ariunbold; and Gansukh, Sukhchuluun, "A Contribution to the Bats Inhabiting Arid Steppe Habitats in Central Mongolia" (2010). Erforschung biologischer Ressourcen der Mongolei / Exploration into the Biological Resources of Mongolia, ISSN 0440-1298. 65. http://digitalcommons.unl.edu/biolmongol/65 This Article is brought to you for free and open access by the Institut für Biologie der Martin-Luther-Universität Halle-Wittenberg at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Erforschung biologischer Ressourcen der Mongolei / Exploration into the Biological Resources of Mongolia, ISSN 0440-1298 by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Copyright 2010, Martin-Luther-Universität Halle Wittenberg, Halle (Saale). Used by permission. Erforsch. biol. Ress. Mongolei (Halle/Saale) 2010 (11): 329-340 A contribution to the bats inhabiting arid steppe habitats in central Mongolia B. Nyambayar, J. Ariunbold & G. Sukhchuluun Abstract Bats of the steppe habitat require particular attention in Mongolia because of increasingly frequent droughts and the looming specter of global warming which could have a devastating impact on their population. -
Background on Swarms the Problem the Algorithm Tested On: Boids
Anomaly Detection in Swarm Robotics: What if a member is hacked? Dan Cronce, Dr. Andrew Williams, Dr. Debbie Perouli Background on Swarms The Problem Finding Absolute Positions of Others ● Swarm - a collection of homogeneous individuals who The messages broadcasted from each swarm member contain In swarm intelligence, each member locally contributes to the locally interact global behavior. If a member were to be maliciously positions from its odometry. However, we cannot trust the controlled, its behavior could be used to control a portion of member we’re monitoring, so we must have a another way of ● Swarm Intelligence - the collective behavior exhibited by the global behavior. This research attempts to determine how finding the position of the suspect. Our method is to find the a swarm one member can determine anomalous behavior in another. distance from three points using the signal strength of its WiFi However, a problem arises: and then using trilateration to determine its current position. Swarm intelligence algorithms enjoy many benefits such as If all we’re detecting is anomalous behavior, how do we tell the scalability and fault tolerance. However, in order to achieve difference between a fault and being hacked? these benefits, the algorithm should follow four rules: Experimental Setting From an outside perspective, we can’t tell if there’s a problem Currently, we have a large, empty space for the robots to roam. with the sensors, the motor, or whether the device is being ● Local interactions only - there cannot be a global store of We plan to set up devices to act as wifi trilateration servers, information or a central server to command the swarm manipulated. -
The First Record of Alpine Long-Eared Bat Plecotus Macrobullaris in Serbia
Turkish Journal of Zoology Turk J Zool (2016) 40: 984-988 http://journals.tubitak.gov.tr/zoology/ © TÜBİTAK Short Communication doi:10.3906/zoo-1505-20 The first record of alpine long-eared bat Plecotus macrobullaris in Serbia 1,2, 2 3 4 2,4 Ivana BUDINSKI *, Branko KARAPANDŽA , Vukašin JOSIPOVIĆ , Jelena JOVANOVIĆ , Milan PAUNOVIĆ 1 Department of Genetic Research, Institute for Biological Research “Siniša Stanković”, University of Belgrade, Belgrade, Serbia 2 Wildlife Conservation Society “Mustela”, Belgrade, Serbia 3 Biological Research Society “Josif Pančić”, Belgrade, Serbia 4 Department of Biological Collections, Natural History Museum, Belgrade, Serbia Received: 13.05.2015 Accepted/Published Online: 10.12.2015 Final Version: 06.12.2016 Abstract: An inventory of bat species in the Mileševka river gorge (southwestern Serbia) was conducted in July 2013. There were six bat species recorded in the cave at the Ćetanica plateau using a mist-netting technique. In this paper we report the first finding of Plecotus macrobullaris in Serbia, contributing to the knowledge of its distribution area. The bat fauna of Serbia now contains 30 species. Key words: Plecotus macrobullaris, Serbia, distribution, Ćetanica plateau The members of the genus Plecotus (long-eared bats) Jadovnik mountains slopes are mainly covered with spruce are widely distributed in the Palearctic (Spitzenberger et and subalpine beech forests (Mišić, 1983). The greatest al., 2006), and six European species have been confirmed part of the gorge is covered with thermophilous forests to date. Phylogenetic analyses revealed the existence of two and shrublands, while mesophilous beech, beech–spruce– major lineages of long-eared bats in Europe: the ‘auritus fir, and even chestnut forests are found at lower elevations group’ (P. -
Cave Bat Species in Mongolia
Cave bat species in Mongolia Ariunbold Jargalsaikhan Department of Biology, School of Mathematic and Natural Science, Mongolian National University of Education. [email protected] +976 99192925 Introduction Total of 19 bat species were recorded in Mongolia and most of them are resident species. A few them are migrant bat species, but never studied before migration of the bat species. There are about 1000 caves in Mongolia and 200 of them considered larger caves in size. Our recent study was determined hibernating places of migrant bat species. In Mongolia, there is almost no studies have been reported about biology of caves; however, Germany researcher M.Stubbe is recorded and ringed an Eastern water bat from cave Havtsal near Chono Kharaikh river in 1977. Our recent studies Ognev’s long-eared bat are recorded 10 species of bats from 14 caves. Conditions of Khevtee bosoo cave Hibernating and habitat conditions of bats, Hibernating and habitat conditions of Steppe environment of Khuit cave Methods whiskered bat, environment of Shar khanan cave In winter time, we have been searched for bats in gaps of walls, clump and ceiling of caves with flash light, and in summer time, just walk in cave and searched for bats and also closing the cave entrance and captured with mist net. All morphometrics of bats were measured and released after ringed the bats. Results and Conclusion The bat species were hibernated Dayandeerkhi, Khuit and Soogt in northern region, and also Tsagaandel, Shar khanan, Taliin agui in the southern region of Mongolia. Soogt cave is 72 m deep vertical shaped (Avirmed, 2008) and Ognev’s long-eared bat (Plecotus ognevi) hibernates there.