15,046 research outputs found

    The Effects of Owl Decoys and Non-threatening Objects on Bird Feeding Behavior

    Get PDF
    As traditional agricultural practices change, the scarecrow has also been renovated and modernized to include mock natural predators, such as owls, hawks, and falcons instead of comical human representations. These facsimiles represent an excellent opportunity to examine anti-predatory tactics and vigilance behavior in birds as a response to perceived threats. In this study, we tested songbird reaction to an owl decoy that mimicked the presence of a predator and to a non-threatening object placed in an oak woodland within Oregon’s Willamette Valley. Frequency of bird visitations to bird feeders when either a plastic owl or a cardboard box of similar size was used to examine the effect of the presence of a predator on bird feeding behavior. We hypothesized that introduction of a model owl would reduce the number of birds observed at a nearby feeder, but a cardboard box would not have a significant effect on bird presence. Using paired t-tests, we determined that a false predator was effective in deterring bird species from feeding, while a box was not

    Talking Helps: Evolving Communicating Agents for the Predator-Prey Pursuit Problem

    Get PDF
    We analyze a general model of multi-agent communication in which all agents communicate simultaneously to a message board. A genetic algorithm is used to evolve multi-agent languages for the predator agents in a version of the predator-prey pursuit problem. We show that the resulting behavior of the communicating multi-agent system is equivalent to that of a Mealy finite state machine whose states are determined by the agents’ usage of the evolved language. Simulations show that the evolution of a communication language improves the performance of the predators. Increasing the language size (and thus increasing the number of possible states in the Mealy machine) improves the performance even further. Furthermore, the evolved communicating predators perform significantly better than all previous work on similar preys. We introduce a method for incrementally increasing the language size which results in an effective coarse-to-fine search that significantly reduces the evolution time required to find a solution. We present some observations on the effects of language size, experimental setup, and prey difficulty on the evolved Mealy machines. In particular, we observe that the start state is often revisited, and incrementally increasing the language size results in smaller Mealy machines. Finally, a simple rule is derived that provides a pessimistic estimate on the minimum language size that should be used for any multi-agent problem

    Cellular Automata Model of Macroevolution

    Full text link
    In this paper I describe a cellular automaton model of a multi-species ecosystem, suitable for the study of emergent properties of macroevolution. Unlike majority of ecological models, the number of coexisting species is not fixed. Starting from one common ancestor they appear by "mutations" of existent species, and then survive or extinct depending on the balance of local ecological interactions. Monte-Carlo numerical simulations show that this model is able to qualitatively reproduce phenomena that have been observed in other models and in nature.Comment: 8 pages, 3 figures, Fourteenth National Conference on Application of Mathematics in Biology and Medicine, Leszno 2008 (POLAND

    Anti-predator behaviour in the freshwater gastropod Lymnaea stagnalis

    Get PDF
    The freshwater gastropod Lymnaea stagnalis was used as a model organism to investigate the mechanisms employed by prey species to fine-tune anti-predator behaviour to match their environment. Lymnaea stagnalis was found to exhibit both genetic adaptation of innate responses and also induced responses to predator cues. Snails were also capable of responding to predation cues via associative learning dependent on recent experience. Constitutive responses were found to differ between populations depending on the predator regime that the population experienced in the wild. Artificial selection produced in only two generations a difference in the magnitude of response between high and low response selected lines equal to those seen between field populations in two generations. At the same time these selected lines maintained phenotypic plasticity and responded to exposure to predator cues during development. This developmental plasticity led to an increased response to predation cues in the low selected line equivilent to that in the high response selection line; a lack of induced change in behaviour in the high response selection line suggested a physiological limitation on the maximum anti-predator response. The response in the low selection lines indicates that plasticity in anti-predator behaviour could allow individuals with low innate responses to compensate with high levels of induced response. Finally, L. stagnalis was able to utilise alarm cues from prey guild members (i.e. other freshwater gastropods) to assess predation risk, a response that was dependent on the phylogenetic relationship between L. stagnalis and the species producing the alarm cue. However, this response was dependent on whether the species was found sympatrically ( cohabiting the same water body) with L. stagnalis. Together, the rapid microevolution of constitutive responses in L. stagnalis, its ability to show induced responses and associative learning indicates that this species may be able to respond rapidly to a novel predation environment, and therefore allow colonistion of new habitats or identification of novel predators

    A new approach to particle swarm optimization algorithm

    Get PDF
    Particularly interesting group consists of algorithms that implement co-evolution or co-operation in natural environments, giving much more powerful implementations. The main aim is to obtain the algorithm which operation is not influenced by the environment. An unusual look at optimization algorithms made it possible to develop a new algorithm and its metaphors define for two groups of algorithms. These studies concern the particle swarm optimization algorithm as a model of predator and prey. New properties of the algorithm resulting from the co-operation mechanism that determines the operation of algorithm and significantly reduces environmental influence have been shown. Definitions of functions of behavior scenarios give new feature of the algorithm. This feature allows self controlling the optimization process. This approach can be successfully used in computer games. Properties of the new algorithm make it worth of interest, practical application and further research on its development. This study can be also an inspiration to search other solutions that implementing co-operation or co-evolution.Angeline, P. (1998). Using selection to improve particle swarm optimization. In Proceedings of the IEEE congress on evolutionary computation, Anchorage (pp. 84–89).Arquilla, J., & Ronfeldt, D. (2000). Swarming and the future of conflict, RAND National Defense Research Institute, Santa Monica, CA, US.Bessaou, M., & Siarry, P. (2001). A genetic algorithm with real-value coding to optimize multimodal continuous functions. Structural and Multidiscipline Optimization, 23, 63–74.Bird, S., & Li, X. (2006). Adaptively choosing niching parameters in a PSO. In Proceedings of the 2006 genetic and evolutionary computation conference (pp. 3–10).Bird, S., & Li, X. (2007). Using regression to improve local convergence. In Proceedings of the 2007 IEEE congress on evolutionary computation (pp. 592–599).Blackwell, T., & Bentley, P. (2002). Dont push me! Collision-avoiding swarms. In Proceedings of the IEEE congress on evolutionary computation, Honolulu (pp. 1691–1696).Brits, R., Engelbrecht, F., & van den Bergh, A. P. (2002). Solving systems of unconstrained equations using particle swarm optimization. In Proceedings of the 2002 IEEE conference on systems, man, and cybernetics (pp. 102–107).Brits, R., Engelbrecht, A., & van den Bergh, F. (2002). A niching particle swarm optimizer. In Proceedings of the fourth asia-pacific conference on simulated evolution and learning (pp. 692–696).Chelouah, R., & Siarry, P. (2000). A continuous genetic algorithm designed for the global optimization of multimodal functions. Journal of Heuristics, 6(2), 191–213.Chelouah, R., & Siarry, P. (2000). Tabu search applied to global optimization. European Journal of Operational Research, 123, 256–270.Chelouah, R., & Siarry, P. (2003). Genetic and Nelder–Mead algorithms hybridized for a more accurate global optimization of continuous multiminima function. European Journal of Operational Research, 148(2), 335–348.Chelouah, R., & Siarry, P. (2005). A hybrid method combining continuous taboo search and Nelder–Mead simplex algorithms for the global optimization of multiminima functions. European Journal of Operational Research, 161, 636–654.Chen, T., & Chi, T. (2010). On the improvements of the particle swarm optimization algorithm. Advances in Engineering Software, 41(2), 229–239.Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58–73.Fan, H., & Shi, Y. (2001). Study on Vmax of particle swarm optimization. In Proceedings of the workshop particle swarm optimization, Indianapolis.Gao, H., & Xu, W. (2011). Particle swarm algorithm with hybrid mutation strategy. Applied Soft Computing, 11(8), 5129–5142.Gosciniak, I. (2008). Immune algorithm in non-stationary optimization task. In Proceedings of the 2008 international conference on computational intelligence for modelling control & automation, CIMCA ’08 (pp. 750–755). Washington, DC, USA: IEEE Computer Society.He, Q., & Wang, L. (2007). An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence, 20(1), 89–99.Higashitani, M., Ishigame, A., & Yasuda, K., (2006). Particle swarm optimization considering the concept of predator–prey behavior. In 2006 IEEE congress on evolutionary computation (pp. 434–437).Higashitani, M., Ishigame, A., & Yasuda, K. (2008). Pursuit-escape particle swarm optimization. IEEJ Transactions on Electrical and Electronic Engineering, 3(1), 136–142.Hu, X., & Eberhart, R. (2002). Multiobjective optimization using dynamic neighborhood particle swarm optimization. In Proceedings of the evolutionary computation on 2002. CEC ’02. Proceedings of the 2002 congress (Vol. 02, pp. 1677–1681). Washington, DC, USA: IEEE Computer Society.Hu, X., Eberhart, R., & Shi, Y. (2003). Engineering optimization with particle swarm. In IEEE swarm intelligence symposium, SIS 2003 (pp. 53–57). Indianapolis: IEEE Neural Networks Society.Jang, W., Kang, H., Lee, B., Kim, K., Shin, D., & Kim, S. (2007). Optimized fuzzy clustering by predator prey particle swarm optimization. In IEEE congress on evolutionary computation, CEC2007 (pp. 3232–3238).Kennedy, J. (2000). Stereotyping: Improving particle swarm performance with cluster analysis. In Proceedings of the 2000 congress on evolutionary computation (pp. 1507–1512).Kennedy, J., & Mendes, R. (2002). Population structure and particle swarm performance. In IEEE congress on evolutionary computation (pp. 1671–1676).Kuo, H., Chang, J., & Shyu, K. (2004). A hybrid algorithm of evolution and simplex methods applied to global optimization. Journal of Marine Science and Technology, 12(4), 280–289.Leontitsis, A., Kontogiorgos, D., & Pange, J. (2006). Repel the swarm to the optimum. Applied Mathematics and Computation, 173(1), 265–272.Li, X. (2004). Adaptively choosing neighborhood bests using species in a particle swarm optimizer for multimodal function optimization. In Proceedings of the 2004 genetic and evolutionary computation conference (pp. 105–116).Li, C., & Yang, S. (2009). A clustering particle swarm optimizer for dynamic optimization. In Proceedings of the 2009 congress on evolutionary computation (pp. 439–446).Liang, J., Suganthan, P., & Deb, K. (2005). Novel composition test functions for numerical global optimization. In Proceedings of the swarm intelligence symposium [Online]. Available: .Liang, J., Qin, A., Suganthan, P., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10(3), 281–295.Lovbjerg, M., & Krink, T. (2002). Extending particle swarm optimizers with self-organized criticality. In Proceedings of the congress on evolutionary computation, Honolulu (pp. 1588–1593).Lung, R., & Dumitrescu, D. (2007). A collaborative model for tracking optima in dynamic environments. In Proceedings of the 2007 congress on evolutionary computation (pp. 564–567).Mendes, R., Kennedy, J., & Neves, J. (2004). The fully informed particle swarm: simpler, maybe better. IEEE Transaction on Evolutionary Computation, 8(3), 204–210.Miranda, V., & Fonseca, N. (2002). New evolutionary particle swarm algorithm (EPSO) applied to voltage/VAR control. In Proceedings of the 14th power systems computation conference, Seville, Spain [Online] Available: .Parrott, D., & Li, X. (2004). A particle swarm model for tracking multiple peaks in a dynamic environment using speciation. In Proceedings of the 2004 congress on evolutionary computation (pp. 98–103).Parrott, D., & Li, X. (2006). Locating and tracking multiple dynamic optima by a particle swarm model using speciation. In IEEE transaction on evolutionary computation (Vol. 10, pp. 440–458).Parsopoulos, K., & Vrahatis, M. (2004). UPSOA unified particle swarm optimization scheme. Lecture Series on Computational Sciences, 868–873.Passaroand, A., & Starita, A. (2008). Particle swarm optimization for multimodal functions: A clustering approach. Journal of Artificial Evolution and Applications, 2008, 15 (Article ID 482032).Peram, T., Veeramachaneni, K., & Mohan, C. (2003). Fitness-distance-ratio based particle swarm optimization. In Swarm intelligence symp. (pp. 174–181).Sedighizadeh, D., & Masehian, E. (2009). Particle swarm optimization methods, taxonomy and applications. International Journal of Computer Theory and Engineering, 1(5), 1793–8201.Shi, Y., & Eberhart, R. (2001). Particle swarm optimization with fuzzy adaptive inertia weight. In Proceedings of the workshop particle swarm optimization, Indianapolis (pp. 101–106).Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. In Proceedings of IEEE International Conference on Evolutionary Computation (pp. 69–73). Washington, DC, USA: IEEE Computer Society.Thomsen, R. (2004). Multimodal optimization using crowding-based differential evolution. In Proceedings of the 2004 congress on evolutionary computation (pp. 1382–1389).Trojanowski, K., & Wierzchoń, S. (2009). Immune-based algorithms for dynamic optimization. Information Sciences, 179(10), 1495–1515.Tsoulos, I., & Stavrakoudis, A. (2010). Enhancing PSO methods for global optimization. Applied Mathematics and Computation, 216(10), 2988–3001.van den Bergh, F., & Engelbrecht, A. (2004). A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8, 225–239.Wolpert, D., & Macready, W. (1997). No free lunch theorems for optimization. IEEE Transaction on Evolutionary Computation, 1(1), 67–82.Xie, X., Zhang, W., & Yang, Z. (2002). Dissipative particle swarm optimization. In Proceedings of the congress on evolutionary computation (pp. 1456–1461).Yang, S., & Li, C. (2010). A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. In IEEE Trans. on evolutionary computation (Vol. 14, pp. 959–974).Kuo, H., Chang, J., & Liu, C. (2006). Particle swarm optimization for global optimization problems. Journal of Marine Science and Technology, 14(3), 170–181

    The role of sand lances (Ammodytes sp.) in the Northwest Atlantic ecosystem: a synthesis of current knowledge with implications for conservation and management

    Get PDF
    © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Staudinger, M. D., Goyert, H., Suca, J. J., Coleman, K., Welch, L., Llopiz, J. K., Wiley, D., Altman, I., Applegate, A., Auster, P., Baumann, H., Beaty, J., Boelke, D., Kaufman, L., Loring, P., Moxley, J., Paton, S., Powers, K., Richardson, D., Robbins, J., Runge, J., Smith, B., Spiegel, C., & Steinmetz, H. The role of sand lances (Ammodytes sp.) in the Northwest Atlantic ecosystem: a synthesis of current knowledge with implications for conservation and management. Fish and Fisheries, 00, (2020): 1-34, doi:10.1111/faf.12445.The American sand lance (Ammodytes americanus, Ammodytidae) and the Northern sand lance (A. dubius, Ammodytidae) are small forage fishes that play an important functional role in the Northwest Atlantic Ocean (NWA). The NWA is a highly dynamic ecosystem currently facing increased risks from climate change, fishing and energy development. We need a better understanding of the biology, population dynamics and ecosystem role of Ammodytes to inform relevant management, climate adaptation and conservation efforts. To meet this need, we synthesized available data on the (a) life history, behaviour and distribution; (b) trophic ecology; (c) threats and vulnerabilities; and (d) ecosystem services role of Ammodytes in the NWA. Overall, 72 regional predators including 45 species of fishes, two squids, 16 seabirds and nine marine mammals were found to consume Ammodytes. Priority research needs identified during this effort include basic information on the patterns and drivers in abundance and distribution of Ammodytes, improved assessments of reproductive biology schedules and investigations of regional sensitivity and resilience to climate change, fishing and habitat disturbance. Food web studies are also needed to evaluate trophic linkages and to assess the consequences of inconsistent zooplankton prey and predator fields on energy flow within the NWA ecosystem. Synthesis results represent the first comprehensive assessment of Ammodytes in the NWA and are intended to inform new research and support regional ecosystem‐based management approaches.This manuscript is the result of follow‐up work stemming from a working group formed at a two‐day multidisciplinary and international workshop held at the Parker River National Wildlife Refuge, Massachusetts in May 2017, which convened 55 experts scientists, natural resource managers and conservation practitioners from 15 state, federal, academic and non‐governmental organizations with interest and expertise in Ammodytes ecology. Support for this effort was provided by USFWS, NOAA Stellwagen Bank National Marine Sanctuary, U.S. Department of the Interior, U.S. Geological Survey, Northeast Climate Adaptation Science Center (Award # G16AC00237), an NSF Graduate Research Fellowship to J.J.S., a CINAR Fellow Award to J.K.L. under Cooperative Agreement NA14OAR4320158, NSF award OCE‐1325451 to J.K.L., NSF award OCE‐1459087 to J.A.R, a Regional Sea Grant award to H.B. (RNE16‐CTHCE‐l), a National Marine Sanctuary Foundation award to P.J.A. (18‐08‐B‐196) and grants from the Mudge Foundation. The contents of this paper are the responsibility of the authors and do not necessarily represent the views of the National Oceanographic and Atmospheric Administration, U.S. Fish and Wildlife Service, New England Fishery Management Council and Mid‐Atlantic Fishery Management Council. This manuscript is submitted for publication with the understanding that the United States Government is authorized to reproduce and distribute reprints for Governmental purposes. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government

    Phenotypic and social effects on behavioural trade-offs in Eurasian perch

    Get PDF
    Trading between conflicting demands is a fundamental part in how animals interact with its environment and social surrounding. Knowledge of what factors that are affecting behavioural decisions is central in our understanding of animal adaptation and ecology. This thesis summarizes a series of behavioural experiments investigating how animals compromise behaviours depending on environmental background and context. The focus is on within- and between-population variation in risk-taking and social trade-offs in young of the year and one year old Eurasian perch. Perch behaviour was quantified by observational studies in aquaria, using standardized assays that captured perch boldness and sociability. Perch from different predation backgrounds were compared in common garden experiments, as well as in multi-year inter-population comparisons, to study influence of predation experience on risk-taking phenotype. Results demonstrate predation as an important factor underlying how perch balance risk. Variation in risk-taking phenotype could to a large extent be explained by individual differences in experience of predation, rather than by fixed inherited responses caused by divergent selection. Experience of predation had long lasting effects on perch boldness, but perch were also able to quickly adjust phenotype in response to current conditions, indicating temporal flexibility in how experience shape behaviour. Social context influenced behaviour, with fish being bolder in larger group, and showing higher behavioural conformity. Occurrence of consistent individual variation in risk-taking and social behaviour could be established, confirming the existence of a personality dimension in perch behaviour. The thesis concludes that variation in how perch trade-off conflicting behaviours exists at multiple levels, from population to individual. Behavioural plasticity, even in strongly fitness related traits, is evident, although potential behavioural constraints in the form of consistent individuality is also present
    corecore