Coevolution of Patch Selection in Stochastic Environments

被引:1
|
作者
Schreiber, Sebastian J. [1 ]
Hening, Alexandru [2 ]
Nguyen, Dang H. [3 ]
机构
[1] Univ Calif Davis, Dept Evolut & Ecol, Davis, CA 95616 USA
[2] Texas A&M Univ, Dept Math, College Stn, TX 77843 USA
[3] Univ Alabama, Dept Math, Tuscaloosa, AL 35487 USA
来源
AMERICAN NATURALIST | 2023年 / 202卷 / 02期
基金
美国国家科学基金会;
关键词
coevolution; habitat selection; environmental stochasticity; portfolio theory; evolutionarily stable strategy; ENEMY-FREE-SPACE; SOURCE-SINK DYNAMICS; POPULATION GENETIC-ANALYSIS; HABITAT SELECTION; EVOLUTIONARY STABILITY; SPATIAL HETEROGENEITY; ELEVATIONAL GRADIENT; CONTRARY CHOICES; DISPERSAL; PREY;
D O I
10.1086/725079
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Species interact in landscapes where environmental conditions vary in time and space. This variability impacts how species select habitat patches. Under equilibrium conditions, evolution of this patch selection can result in ideal free distributions where per capita growth rates are zero in occupied patches and negative in unoccupied patches. These ideal free distributions, however, do not explain why species occupy sink patches, why competitors have overlapping spatial ranges, or why predators avoid highly productive patches. To understand these patterns, we solve for coevolutionarily stable strategies (coESSs) of patch selection for multispecies stochastic Lotka-Volterra models accounting for spatial and temporal heterogeneity. In occupied patches at the coESS, we show that the differences between the local contributions to the mean and the variance of the long-term population growth rate are equalized. Applying this characterization to models of antagonistic interactions reveals that environmental stochasticity can partially exorcize the ghost of competition past, select for new forms of enemy-free and victimless space, and generate hydra effects over evolutionary timescales. Viewing our results through the economic lens of modern portfolio theory highlights why the coESS for patch selection is often a bet-hedging strategy coupling stochastic sink populations. Our results highlight how environmental stochasticity can reverse or amplify evolutionary outcomes as a result of species interactions or spatial heterogeneity.
引用
收藏
页码:122 / 139
页数:18
相关论文
共 50 条
  • [11] Coevolution of Neural Networks for Agents and Environments
    Chigot, Estelle
    Wilson, Dennis G.
    PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022, 2022, : 2306 - 2309
  • [12] On the Behavior of Cooperative Coevolution in Dynamic Environments
    Au, Chun-Kit
    Leung, Ho-Fung
    2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8, 2008, : 2827 - 2836
  • [13] A Learning Automata Based Solution to Service Selection in Stochastic Environments
    Yazidi, Anis
    Granmo, Ole-Christoffer
    Oommen, B. John
    TRENDS IN APPLIED INTELLIGENT SYSTEMS, PT III, PROCEEDINGS, 2010, 6098 : 209 - +
  • [14] Service supplier selection under fuzzy and stochastic uncertain environments
    Liu, Meng
    Wang, Xiaolin
    Li, Yupeng
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2022, 42 (03) : 1301 - 1315
  • [15] The Coevolution of Industries and Important Features of Their Environments
    Murmann, Johann Peter
    ORGANIZATION SCIENCE, 2013, 24 (01) : 58 - 78
  • [16] Differential Evolution with Stochastic Selection for Uncertain Environments: A Smart Grid Application
    Palakonda, Vikas
    Awad, Noor H.
    Mallipeddi, Rammohan
    Ali, Mostafa Z.
    Veluvolu, K. C.
    Suganthan, P. N.
    2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2018, : 400 - 406
  • [17] Service selection in stochastic environments: a learning-automaton based solution
    Anis Yazidi
    Ole-Christoffer Granmo
    B. John Oommen
    Applied Intelligence, 2012, 36 : 617 - 637
  • [18] Coevolution of patch-type dependent emigration and patch-type dependent immigration
    Weigang, Helene C.
    JOURNAL OF THEORETICAL BIOLOGY, 2017, 426 : 140 - 151
  • [19] Horn growth in male pronghornsAntilocapra americana: selection for precocialmaturation in stochastic environments
    Carl D. Mitchell
    Christine R. Maher
    Acta Theriologica, 2006, 51 : 405 - 409
  • [20] Service selection in stochastic environments: a learning-automaton based solution
    Yazidi, Anis
    Granmo, Ole-Christoffer
    Oommen, B. John
    APPLIED INTELLIGENCE, 2012, 36 (03) : 617 - 637