Landscape composition and pollinator traits interact to influence pollination success in an individual-based model

被引:6
|
作者
Kortsch, Susanne [1 ]
Saravia, Leonardo [2 ]
Cirtwill, Alyssa R. R. [1 ]
Timberlake, Thomas [3 ]
Memmott, Jane [3 ]
Kendall, Liam [4 ]
Roslin, Tomas [1 ,5 ,6 ]
Strona, Giovanni [5 ,7 ]
机构
[1] Univ Helsinki, Fac Agr & Forestry, Spatial Foodweb Ecol Grp, Dept Agr Sci, Helsinki, Finland
[2] Ctr Austral Invest Cient Consejo Nacl Invest Cient, Ushuaia, Argentina
[3] Univ Bristol, Sch Biol Sci, Bristol, England
[4] Lund Univ, Ctr Environm & Climate Sci, Lund, Sweden
[5] Univ Helsinki, Fac Biol & Environm Sci, Organismal & Evolutionary Biol Res Programme, Helsinki, Finland
[6] Swedish Agr Univ, Dept Ecol, Uppsala, Sweden
[7] European Commiss, Joint Res Ctr, Ispra, Italy
基金
欧洲研究理事会; 英国自然环境研究理事会; 美国国家科学基金会; 芬兰科学院;
关键词
agent-based model; habitat heterogeneity; movement ecology; Netlogo; patch size; visitation rate; INTERSPECIFIC POLLEN TRANSFER; BODY-SIZE; CONSEQUENCES; COMPETITION; DIVERSITY; SPEED; BEES;
D O I
10.1111/1365-2435.14353
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
The arrangement of plant species within a landscape influences pollination via changes in pollinator movement trajectories and plant-pollinator encounter rates. Yet the combined effects of landscape composition and pollinator traits (especially specialisation) on pollination success remain hard to quantify empirically.We used an individual-based model to explore how landscape and pollinator specialisation (degree) interact to influence pollination. We modelled variation in the landscape by generating gradients of plant species intermixing-from no mixing to complete intermixing. Furthermore, we varied the level of pollinator specialisation by simulating plant-pollinator (six to eight species) networks of different connectance. We then compared the impacts of these drivers on three proxies for pollination: visitation rate, number of consecutive visits to the focal plant species and expected number of plants pollinated.We found that the spatial arrangements of plants and pollinator degree interact to determine pollination success, and that the influence of these drivers on pollination depends on how pollination is estimated. For most pollinators, visitation rate increases in more plant mixed landscapes. Compared to the two more functional measures of pollination, visitation rate overestimates pollination service. This is particularly severe in landscapes with high plant intermixing and for generalist pollinators. Interestingly, visitation rate is less influenced by pollinator traits (pollinator degree and body size) than are the two functional metrics, likely because 'visitation rate' ignores the order in which pollinators visit plants. However, the visitation sequence order is crucial for the expected number of plants pollinated, since only prior visits to conspecific individuals can contribute to pollination. We show here that this order strongly depends on the spatial arrangements of plants, on pollinator traits and on the interaction between them.Taken together, our findings suggest that visitation rate, the most commonly used proxy for pollination in network studies, should be complemented with more functional metrics which reflect the frequency with which individual pollinators revisit the same plant species. Our findings also suggest that measures of landscape structure such as plant intermixing and density-in combination with pollinators' level of specialism-can improve estimates of the probability of pollination.
引用
收藏
页码:2056 / 2071
页数:16
相关论文
共 50 条
  • [41] An individual-based model for predicting the prevalence of depression
    Loula, R.
    Monteiro, L. H. A.
    ECOLOGICAL COMPLEXITY, 2019, 38 : 168 - 172
  • [42] Stochastic individual-based model of spread of tuberculosis
    Pertsev, N. V.
    Leonenko, V. N.
    RUSSIAN JOURNAL OF NUMERICAL ANALYSIS AND MATHEMATICAL MODELLING, 2009, 24 (04) : 341 - 360
  • [43] An individual-based model of alpine plant distributions
    Humphries, HC
    Coffin, DP
    Lauenroth, WK
    ECOLOGICAL MODELLING, 1996, 84 (1-3) : 99 - 126
  • [44] A REVIEW ON THE DEVELOPMENT OF INDIVIDUAL-BASED MODEL IN ECOLOGY
    Zhu, Meixia
    Pei, Yongzhen
    Li, Changguo
    COMMUNICATIONS IN MATHEMATICAL BIOLOGY AND NEUROSCIENCE, 2016,
  • [45] A mechanistic Individual-based Model of microbial communities
    Jayathilake, Pahala Gedara
    Gupta, Prashant
    Li, Bowen
    Madsen, Curtis
    Oyebamiji, Oluwole
    Gonzalez-Cabaleiro, Rebeca
    Rushton, Steve
    Bridgens, Ben
    Swailes, David
    Allen, Ben
    McGough, A. Stephen
    Zuliani, Paolo
    Ofiteru, Irina Dana
    Wilkinson, Darren
    Chen, Jinju
    Curtis, Tom
    PLOS ONE, 2017, 12 (08):
  • [46] Individual-based model for coevolving competing populations
    Charret, I. C.
    Louzada, J. N. C.
    Costa, A. T., Jr.
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2007, 385 (01) : 249 - 254
  • [48] Linkages - An individual-based forest ecosystem model
    Post, WM
    Pastor, J
    CLIMATIC CHANGE, 1996, 34 (02) : 253 - 261
  • [49] An individual-based model of influenza in nosocomial environments
    Ong, Boon Som
    Chen, Mark
    Lee, Vernon
    Tay, Joc Cing
    COMPUTATIONAL SCIENCE - ICCS 2008, PT 1, 2008, 5101 : 590 - +
  • [50] Fisher waves: An individual-based stochastic model
    Houchmandzadeh, B.
    Vallade, M.
    PHYSICAL REVIEW E, 2017, 96 (01)