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Relatedness, Parentage, and Philopatry Within a Natterer's Bat
Population Ecology (2018) 60:361–370 https://doi.org/10.1007/s10144-018-0632-7 ORIGINAL ARTICLE Relatedness, parentage, and philopatry within a Natterer’s bat (Myotis nattereri) maternity colony David Daniel Scott1 · Emma S. M. Boston2 · Mathieu G. Lundy1 · Daniel J. Buckley2 · Yann Gager1 · Callum J. Chaplain1 · Emma C. Teeling2 · William Ian Montgomery1 · Paulo A. Prodöhl1 Received: 20 April 2017 / Accepted: 26 September 2018 / Published online: 10 October 2018 © The Author(s) 2018 Abstract Given their cryptic behaviour, it is often difficult to establish kinship within microchiropteran maternity colonies. This limits understanding of group formation within this highly social group. Following a concerted effort to comprehensively sample a Natterer’s bat (Myotis nattereri) maternity colony over two consecutive summers, we employed microsatellite DNA profiling to examine genetic relatedness among individuals. Resulting data were used to ascertain female kinship, parentage, mating strategies, and philopatry. Overall, despite evidence of female philopatry, relatedness was low both for adult females and juveniles of both sexes. The majority of individuals within the colony were found to be unrelated or distantly related. How- ever, parentage analysis indicates the existence of a number of maternal lineages (e.g., grandmother, mother, or daughter). There was no evidence suggesting that males born within the colony are mating with females of the same colony. Thus, in this species, males appear to be the dispersive sex. In the Natterer’s bat, colony formation is likely to be based on the benefits of group living, rather than kin selection. Keywords Chiroptera · Kinship · Natal philopatry · Parentage assignment Introduction of the environment (Schober 1984). -
Dispersal, Philopatry, and the Role of Fissionfusion Dynamics In
MARINE MAMMAL SCIENCE, **(*): ***–*** (*** 2012) C 2012 by the Society for Marine Mammalogy DOI: 10.1111/j.1748-7692.2011.00559.x Dispersal, philopatry, and the role of fission-fusion dynamics in bottlenose dolphins YI-JIUN JEAN TSAI and JANET MANN,1 Reiss Science Building, Rm 406, Department of Biology, Georgetown University, Washington, DC 20057, U.S.A. ABSTRACT In this quantitative study of locational and social dispersal at the individual level, we show that bottlenose dolphins (Tursiops sp.) continued to use their na- tal home ranges well into adulthood. Despite substantial home range overlap, mother–offspring associations decreased after weaning, particularly for sons. These data provide strong evidence for bisexual locational philopatry and mother–son avoidance in bottlenose dolphins. While bisexual locational philopatry offers the benefits of familiar social networks and foraging habitats, the costs of philopatry may be mitigated by reduced mother–offspring association, in which the risk of mother–daughter resource competition and mother–son mating is reduced. Our study highlights the advantages of high fission–fusion dynamics and longitudinal studies, and emphasizes the need for clarity when describing dispersal in this and other species. Key words: bottlenose dolphin, Tursiops sp., association, juvenile, philopatry, locational dispersal, mother-offspring, ranging, site fidelity, social dispersal. Dispersal is a key life history process that affects population demography, spatial distribution, and genetic structure, as well as individual -
Swarming (Bulletin #404) (PDF)
Swarming Apiculture Bulletin #404 Updated: 10/15 Swarming is a natural method of honeybee colonies to reproduce, resulting in the creation of a new honeybee colony in addition to the established colony. Contributing factors to swarming • Crowding - too many bees, food stores and no cell space for the queen to lay eggs in. • Poor air circulation • April-May is swarming season and healthy colonies develop strong swarm impulse. • Inclement weather - crowded bees confined by cold, wet weather will build queen cells and swarm out on the first sunny, warm day. All colonies in similar condition will swarm as soon as weather becomes favorable. • Large amount of drone brood in early spring is a precursor to strong swarm impulse. Catching the swarm A swarm generally emerges from the hive between 11:00AM and 1:00PM and settles close to the apiary for several hours. Allow the swarm to cluster for at least 30 minutes before placing the swarm in a single super with frames, bottom board and hive cover. Leave the hive for the remainder of the day to allow all the bees to enter, before moving to a permanent location. Examine the new colony after a week and then every two weeks from mid May to late June. Look for disease, brood abundance, brood pattern and overall condition of the colony. Add room where necessary, cut queen cells, or divide, to prevent other swarms to develop. Hived swarms have no stored food reserves and may go hungry when there is little forage. When feeding sugar syrup, add antibiotics as a precautionary measure. -
What Is a Social Spider? “Sub”-Social Spiders Eusociality Social Definitions Natural History
What is a Social Spider? • Generally accepted as living in colonies while having generational overlap and exhibiting cooperative brood care and nest maintenance Scott Trageser • Can also have reproductive division of labor and exhibit swarming behavior (Achaearanea wau) • Social hierarchies arise • Species and population dependant • Arguable if certain species qualify as eusocial “Sub”-Social Spiders Eusociality • Sociality also classified by territoriality and • There is cooperative brood-care so it is permanence not each one caring for their own offspring • Can be social on a seasonal basis and • There is an overlapping of generations so have an obligate solitary phase that the colony will sustain for a while, • Individuals can have established allowing offspring to assist parents during territories within the nest or can move their life freely • That there is a reproductive division of • Can have discrete webs connected to labor, i.e. not every individual reproduces other webs in the colony (cooperative?) equally in the group Social Definitions Natural History • Solitary: Showing none of the three features mentioned in the previous slide (most insects) • 23 of over 39,000 spider species are • Sub-social: The adults care for their own young for “social” some period of time (cockroaches) • Many other species are classified as • Communal: Insects use the same composite nest without cooperation in brood care (digger bees) “sub”-social • Quasi-social: Use the same nest and also show cooperative brood care (Euglossine bees and social • Tropical origin. Latitudinal and elevational spiders) distribution constrictions due to prey • Semi-social: in addition to the features in type/abundance quasisocial, they also have a worker caste (Halictid bees) • Emigrate (swarm) after courtship and • Eusocial: In addition to the features of semisocial, copulation but prior to oviposition there is overlap in generations (Honey bees). -
The Influence of Density in Population Dynamics with Strong and Weak Allee Effect
Keya et al. J Egypt Math Soc (2021) 29:4 https://doi.org/10.1186/s42787-021-00114-x Journal of the Egyptian Mathematical Society ORIGINAL RESEARCH Open Access The infuence of density in population dynamics with strong and weak Allee efect Kamrun Nahar Keya1, Md. Kamrujjaman2* and Md. Shafqul Islam3 *Correspondence: [email protected] Abstract 2 Department In this paper, we consider a reaction–difusion model in population dynamics and of Mathematics, University of Dhaka, Dhaka 1000, study the impact of diferent types of Allee efects with logistic growth in the hetero- Bangladesh geneous closed region. For strong Allee efects, usually, species unconditionally die out Full list of author information and an extinction-survival situation occurs when the efect is weak according to the is available at the end of the article resource and sparse functions. In particular, we study the impact of the multiplicative Allee efect in classical difusion when the sparsity is either positive or negative. Nega- tive sparsity implies a weak Allee efect, and the population survives in some domain and diverges otherwise. Positive sparsity gives a strong Allee efect, and the popula- tion extinct without any condition. The infuence of Allee efects on the existence and persistence of positive steady states as well as global bifurcation diagrams is presented. The method of sub-super solutions is used for analyzing equations. The stability condi- tions and the region of positive solutions (multiple solutions may exist) are presented. When the difusion is absent, we consider the model with and without harvesting, which are initial value problems (IVPs) and study the local stability analysis and present bifurcation analysis. -
Comparative Methods Offer Powerful Insights Into Social Evolution in Bees Sarah Kocher, Robert Paxton
Comparative methods offer powerful insights into social evolution in bees Sarah Kocher, Robert Paxton To cite this version: Sarah Kocher, Robert Paxton. Comparative methods offer powerful insights into social evolution in bees. Apidologie, Springer Verlag, 2014, 45 (3), pp.289-305. 10.1007/s13592-014-0268-3. hal- 01234748 HAL Id: hal-01234748 https://hal.archives-ouvertes.fr/hal-01234748 Submitted on 27 Nov 2015 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Apidologie (2014) 45:289–305 Review article * INRA, DIB and Springer-Verlag France, 2014 DOI: 10.1007/s13592-014-0268-3 Comparative methods offer powerful insights into social evolution in bees 1 2 Sarah D. KOCHER , Robert J. PAXTON 1Department of Organismic and Evolutionary Biology, Museum of Comparative Zoology, Harvard University, Cambridge, MA, USA 2Institute for Biology, Martin-Luther-University Halle-Wittenberg, Halle, Germany Received 9 September 2013 – Revised 8 December 2013 – Accepted 2 January 2014 Abstract – Bees are excellent models for studying the evolution of sociality. While most species are solitary, many form social groups. The most complex form of social behavior, eusociality, has arisen independently four times within the bees. -
Interactive Robots in Experimental Biology 3 4 5 6 Jens Krause1,2, Alan F.T
1 2 Interactive Robots in Experimental Biology 3 4 5 6 Jens Krause1,2, Alan F.T. Winfield3 & Jean-Louis Deneubourg4 7 8 9 10 11 12 1Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Department of Biology and 13 Ecology of Fishes, 12587 Berlin, Germany; 14 2Humboldt-University of Berlin, Department for Crop and Animal Sciences, Philippstrasse 15 13, 10115 Berlin, Germany; 16 3Bristol Robotics Laboratory, University of the West of England, Coldharbour Lane, Bristol 17 BS16 1QY, UK; 18 4Unit of Social Ecology, Campus Plaine - CP 231, Université libre de Bruxelles, Bd du 19 Triomphe, B-1050 Brussels - Belgium 20 21 22 23 24 25 26 27 28 Corresponding author: Krause, J. ([email protected]), Leibniz Institute of Freshwater 29 Ecology and Inland Fisheries, Department of the Biology and Ecology of Fishes, 30 Müggelseedamm 310, 12587 Berlin, Germany. 31 32 33 1 33 Interactive robots have the potential to revolutionise the study of social behaviour because 34 they provide a number of methodological advances. In interactions with live animals the 35 behaviour of robots can be standardised, morphology and behaviour can be decoupled (so that 36 different morphologies and behavioural strategies can be combined), behaviour can be 37 manipulated in complex interaction sequences and models of behaviour can be embodied by 38 the robot and thereby be tested. Furthermore, robots can be used as demonstrators in 39 experiments on social learning. The opportunities that robots create for new experimental 40 approaches have far-reaching consequences for research in fields such as mate choice, 41 cooperation, social learning, personality studies and collective behaviour. -
The Use of Swarming Unmanned Vehicles to Support Target
Forthcoming in Proceedings of SPIE Defense & Security Conference, March 2008, Orlando, FL Distributed Pheromone-Based Swarming Control of Unmanned Air and Ground Vehicles for RSTA John A. Sauter, Robert S. Joshua S. Robinson, John Stephanie Riddle Mathews, Andrew Yinger* Moody† Naval Air Systems NewVectors Augusta Systems, Inc. Command (NAVAIR) 3520 Green Ct 3592 Collins Ferry Road 48150 Shaw Road Ann Arbor, MI 48105 Suite 200 Bldg. 2109 Suite S211-21 Morgantown, WV 26505 Patuxent River, MD 20670 Abstract The use of unmanned vehicles in Reconnaissance, Surveillance, and Target Acquisition (RSTA) applications has received considerable attention recently. Cooperating land and air vehicles can support multiple sensor modalities providing pervasive and ubiquitous broad area sensor coverage. However coordination of multiple air and land vehicles serving different mission objectives in a dynamic and complex environment is a challenging problem. Swarm intelligence algorithms, inspired by the mechanisms used in natural systems to coordinate the activities of many entities provide a promising alternative to traditional command and control approaches. This paper describes recent advances in a fully distributed digital pheromone algorithm that has demonstrated its effectiveness in managing the complexity of swarming unmanned systems. The results of a recent demonstration at NASA’s Wallops Island of multiple Aerosonde Unmanned Air Vehicles (UAVs) and Pioneer Unmanned Ground Vehicles (UGVs) cooperating in a coordinated RSTA application are discussed. The vehicles were autonomously controlled by the onboard digital pheromone responding to the needs of the automatic target recognition algorithms. UAVs and UGVs controlled by the same pheromone algorithm self-organized to perform total area surveillance, automatic target detection, sensor cueing, and automatic target recognition with no central processing or control and minimal operator input. -
Breeding Site Fidelity in Southern Elephant Seals of the Falkland Islands
Filippo Galimberti 1 and Simona Sanvito 1,2 1 Elephant Seal Research Group, Sea Lion Island, Falkland Islands 2 Dept. of Biology, Memorial University of Newfoundland, St. John’s NF A1B 3X9, Canada ESRG technical report no. 3 Breeding site fidelity in southern elephant seals of the Falkland Islands Milano, 25th April 2000 © Elephant Seal Research Group – 2000 No part of this report can be cited without written permission from the authors. Address for correspondence: Dr. Filippo Galimberti – Via Buonarroti 35 – 20145 Milano – Italy Phone +39 02 4980504 – Fax +39 02 48008145 – Email [email protected] ABSTRACT Breeding site fidelity, i.e., the tendency to return to the same breeding site for consecutive breeding attempts, is an important component of mammal life history strategies, and seems almost ubiquitous in Pinnipedia species, at least for land breeding ones. Site fidelity may entail significant somatic benefits and costs, and, if coupled with return for first breeding attempt to birth site, may produce a genetic sub-structuring of populations. We present data on female and male site fidelity, together with preliminary information on female phylopatry, of southern elephant seals (Mirounga leonina) of Sea Lion Island, the main breeding site of the species in the Falkland Islands. We found a high level of site fidelity at small scale (hundred of metres) in both sexes, although higher in females, even when considering up to five consecutive breeding seasons of the same individual. We discuss the behavioural and genetic implications of site fidelity, emphasizing that it may affect mating tactics, breeding success, and genetic sub-structuring of local populations. -
On Adaptive Self-Organization in Artificial Robot Organisms
On adaptive self-organization in artificial robot organisms Serge Kernbach∗, Heiko Hamann†, Jurgen¨ Stradner†, Ronald Thenius†, Thomas Schmickl†, Karl Crailsheim†, A.C. van Rossum‡, Michele Sebag§, Nicolas Bredeche§, Yao Yao¶, Guy Baele¶, Yves Van de Peer¶, Jon Timmisk, Maizura Mohktark, Andy Tyrrellk, A.E. Eiben∗∗, S.P. McKibbin††, Wenguo Liu‡‡, Alan F.T. Winfield‡‡ ∗Institute of Parallel and Distributed Systems, University of Stuttgart, Germany, [email protected] †Artificial Life Lab, Karl-Franzens-University Graz, Universitatsplatz 2, A-8010 Graz, Austria, {heiko.hamann,juergen.stradner,ronald.thenius,thomas.schmickl,karl.crailsheim}@uni-graz.at ‡Almende B.V., 3016 DJ Rotterdam, Netherlands, [email protected] §TAO, LRI, Univ. Paris-Sud, CNRS, INRIA Saclay, France, {Michele.Sebag, Nicolas.Bredeche}@lri.fr ¶VIB Department Plant Systems Biology, Ghent University, Belgium, {Yao.Yao, Guy.Baele, Yves.VandePeer}@psb.vib-ugent.be kUniversity of York, York, United Kingdom, {mm520, jt517, amt}@ohm.york.ac.uk ∗∗Free University Amsterdam, [email protected] ††Materials and Engineering Research Institute (MERI), Sheffield Hallam University, [email protected] ‡‡Bristol Robotics Laboratory (BRL), UWE Bristol, {wenguo.liu, alan.winfield}@uwe.ac.uk Abstract—In Nature, self-organization demonstrates very reliable and scalable collective behavior in a distributed fash- ion. In collective robotic systems, self-organization makes it possible to address both the problem of adaptation to quickly changing environment and compliance with user-defined target objectives. This paper describes on-going work on artificial self-organization within artificial robot organisms, performed in the framework of the Symbrion and Replicator European projects. (a) (b) Keywords-adaptive system, self-adaptation, adaptive self- organization, collective robotics, artificial organisms I. -
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. -
Asynchronous Evolution: Emergence of Signal-Based Swarming
Asynchronous Evolution: Emergence of Signal-Based Swarming Olaf Witkowski and Takashi Ikegami University of Tokyo, Japan [email protected] Abstract towards a common direction. The movement itself may differ from species to species. Since Reynolds boids, swarming behavior has often been re- For example, fish and insects swarm in three dimensions, produced in artificial models, but the conditions leading to whereas herds of sheep move only in two dimensions. More- its emergence are still subject to research, with candidates over, the collective motion can have quite diverse dynamics. ranging from obstacle avoidance to virtual leaders. In this While birds migrate in relatively ordered formations with paper, we present a multi-agent model in which individuals develop swarming using only their ability to listen to each constant velocity, fish schools change directions by align- others signals. Our model uses an original asynchronous ge- ing rapidly and keeping their distances, and insects swarms netic algorithm to evolve a population of agents controlled by move in a messy and random-looking way (Budrene et al. artificial neural networks, looking for an invisible resource 1991, Czirok´ et al. 1997, Shimoyama et al. 1996). in a 3D environment. The results demonstrate that agents Numerous evolutionary hypotheses have been proposed use the information exchanged between them via signaling to form temporary leader-follower relations allowing them to to explain swarming behavior across species. These include flock together. more efficient mating, good environment for learning, com- bined search for food resources, and reducing risks of pre- dation (Zaera et al., 1996). Partridge and Pitcher (1979) also Introduction mention energy saving in fish schools by reducing drag.