Evolutionary trends in fish schools in heterogeneous environments

被引:8
|
作者
Reuter, Hauke [1 ,2 ]
Kruse, Maren [1 ,2 ]
Rovellini, Alberto [1 ,2 ]
Breckling, Broder [2 ,3 ]
机构
[1] Leibniz Ctr Trop Marine Ecol ZMT, Fahrenheitstr 6, D-28359 Bremen, Germany
[2] Univ Bremen, D-28359 Bremen, Germany
[3] Univ Vechta, Berlin, Germany
关键词
Fish schools; Individual-based model; Spatial heterogeneity; Food patch; Evolution; SELF-ORGANIZATION; COLLECTIVE BEHAVIOR; EMERGENT PROPERTIES; SIMULATION; MODELS; SIZE; PREY; INFORMATION; PATCHINESS; DENSITY;
D O I
10.1016/j.ecolmodel.2015.09.008
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Individual-based modelling has contributed substantially to the understanding of fish schooling behaviour. Schooling is considered to grant several advantages, such as increased defense against predators and increased foraging success. Whereas the former has been well studied with empirical investigations and different modelling approaches, the latter has not received as much attention. Foraging success is considerably influenced by the emergent property of schools to locate and exploit heterogeneously distributed resources more efficiently than solitary fish. However, successful resource exploitation depends on individual fish properties as well as properties of the school in relation to patch size and spatial distribution of resources. Thus, schooling will be favourable in specific environmental conditions and less efficient in others. We use an individual-based model to assess the foraging efficiency of schooling compared to individual food search under different spatio-temporal distributions of food resources in a dynamic environment. Allowing agents' behaviour to evolve either towards schooling or towards individualism, we demonstrate the adaptation of population characteristics to a particular spatial and temporal distribution of food patches. With our model we show that the environmental configuration of food patches is crucial for schooling fish to be more efficient in foraging. Moreover, patch size must be considerably larger than the extent of the school but small enough for patch boundaries to take effect. The model contributes to a better understanding of the relationships among spatial dynamics and the driving forces behind behavioural adaptation of trophic strategies in schooling fish. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:23 / 35
页数:13
相关论文
共 50 条
  • [1] Distributed Evolutionary Algorithms in Heterogeneous Environments
    Salto, Carolina
    Luna, Francisco
    Alba, Enrique
    2013 EIGHTH INTERNATIONAL CONFERENCE ON P2P, PARALLEL, GRID, CLOUD AND INTERNET COMPUTING (3PGCIC 2013), 2013, : 606 - 611
  • [2] Evolutionary Adaptation in Heterogeneous and Changing Environments
    Chaturvedi, Nandita
    Chatterjee, Purba
    EVOLUTION, 2024,
  • [3] EVOLUTIONARY TRENDS OF NEUROFILAMENT PROTEINS IN FISH
    MENCARELLI, C
    MAGI, B
    MARZOCCHI, B
    CONTORNI, M
    PALLINI, V
    COMPARATIVE BIOCHEMISTRY AND PHYSIOLOGY B-BIOCHEMISTRY & MOLECULAR BIOLOGY, 1991, 100 (04): : 733 - 740
  • [4] Migration dynamics of fish schools in heterothermal environments
    Niwa, HS
    JOURNAL OF THEORETICAL BIOLOGY, 1998, 193 (02) : 215 - 231
  • [5] Evolutionary Dynamics of Collaborative Environments with Heterogeneous Agents
    Somasundaram, Kiran K.
    Baras, John S.
    MED: 2009 17TH MEDITERRANEAN CONFERENCE ON CONTROL & AUTOMATION, VOLS 1-3, 2009, : 121 - 125
  • [6] Navigating Turbulent Environments: Insights from Fish Schools
    Calicchia, Michael
    Ni, Rui
    INTEGRATIVE AND COMPARATIVE BIOLOGY, 2024, 64 : S73 - S73
  • [7] Eco-evolutionary dynamics of dispersal in spatially heterogeneous environments
    Hanski, Ilkka
    Mononen, Tommi
    ECOLOGY LETTERS, 2011, 14 (10) : 1025 - 1034
  • [8] Evolutionary game for task mapping in resource constrained heterogeneous environments
    Madeo, Dario
    Mazumdar, Somnath
    Mocenni, Chiara
    Zingone, Roberto
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2020, 108 : 762 - 776
  • [9] Modeling the evolutionary and ecological consequences of selection and adaptation in heterogeneous environments
    Cohen, Dan
    ISRAEL JOURNAL OF ECOLOGY & EVOLUTION, 2006, 52 (3-4): : 467 - 484
  • [10] Evolutionary Training of Deep Neural Networks on Heterogeneous Computing Environments
    Kalia, Subodh
    Mohan, Chilukuri K.
    Nemani, Ramakrishna
    PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022, 2022, : 2318 - 2321