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  • 1 Benaki Phytopathological Institute, St. Delta 8, Kifissia, 14561, Greece
  • | 2 University of Nottingham, University Park, NG7 2RD, UK
  • | 3 Athens University of Economics and Business, Patission 76, Athens, 10434, Greece
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The dynamics of predator-prey systems relate strongly to the density (in)dependent attributes of the predator’s feeding rate, i.e., its functional response. The outcome of functional response models is often used in theoretical or applied ecology in order to extract information about the mechanisms associated with the feeding behavior of predators. The focus of this study centres upon Holling’s type II functional response model, commonly known as the disc equation, which describes an inverse-density dependent mortality caused by a single predator to its prey. A common method to provide inference on functional response data involves nonlinear least squares optimization, assuming independent Gaussian errors, an assumption often violated in practice due to the heteroscedasticity which is typically present in the data. Moreover, as prey depletion is common in functional response experiments, the differential form of disc equation ought to be used in principle. We introduce a related statistical model and adopt a Bayesian approach for estimating parameters in ordinary differential equation models. In addition, we explore model uncertainty via Bayes factors. Our approach is illustrated via the analysis of several data sets concerning the functional response of a widespread ladybird beetle (Propylea quatuordecimpunctata) to its prey (Aphis fabae), predicting the efficiency of this predator on a common and important aphid species. The results showed that the approach developed in this study is towards a direction for accurate estimation of the parameters that determine the shape of the functional response of a predator without having to make unnecessary assumptions. The R (www.r-project.org) code for fitting the proposed model to experimental data is made freely available.

  • Beddington J. 1975. Mutual interference between parasites or predators and its effect on searching efficiency. J. Anim. Ecol. 44(1):331-340.

    • Search Google Scholar
    • Export Citation
  • Blackman, R.L. and V.F. Eastop. 2000. Aphids on the World's Crops. An Identification and Information Guide. John Wiley & Sons, Chichester.

    • Search Google Scholar
    • Export Citation
  • Bolker, B. 2008. Ecological Models and Data in R. Princeton University Press.

  • Brooks, S., A. Gelman, G. Jones and X.L. Meng. 2011. Handbook of Markov Chain Monte Carlo. Taylor & Francis, Boca Raton.

  • Englund, G., G. Ohlund, C.L. Hein and S. Diehl. 2011. Temperature dependence of the functional response. Ecol. Lett. 14(9):914-921.

  • Fan, Y. and F.L. Petitt. 1994. Parameter estimation of the functional response. Environ. Entomol. 23(4):785-794.

  • Fenlon, J.S. and Faddy M.J. 2006. Modelling predation in functional response Ecol. Model. 198: 154162.

  • Friel, N. and A.N. Pettitt. 2008. Marginal likelihood estimation via power posteriors. J. R. Stat. Soc. B 70(3):589-607.

  • Gamerman, D. and H.F. Lopes. 2006. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference. Chapman and Hall/CRC, Boca Raton.

    • Search Google Scholar
    • Export Citation
  • Gelman, A., G. Roberts and W. Gilks. 1996. Efficient metropolis jumping rules. In Bernado, J.M. et al. (eds), Bayesian Statistics, volume 5, page 599. Oxford Univ. Press, Oxford.

    • Search Google Scholar
    • Export Citation
  • Gelman, A., W.R. Gilks and G. Roberts. 1997. Weak convergence and optimal scaling of random walk Metropolis algorithms. Ann. Appl. Probab. 7(1):110-120.

    • Search Google Scholar
    • Export Citation
  • Green, P.J. 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82(4):711-732.

  • Hassell, M., J. Lawton and J. Beddington. 1977. Sigmoid functional responses by invertebrate predators and parasitoids. J. Anim. Ecol. 46(1):249-262.

    • Search Google Scholar
    • Export Citation
  • Hastings, W. 1970. Monte Carlo samping methods using Markov chains and their applications. Biometrika 57: 97109.

  • Hodek, I., H.F. van Emden and A. Honek. 2012. Ecology and Behaviour of the Ladybird Beetles (Coccinellidae). Wiley-Blackwell, Chichester.

    • Search Google Scholar
    • Export Citation
  • Holling, C.S. 1959a. The components of predation as revealed by a study of small-mammal predation of the European pine sawfly. Can. Entomol. 91:293320.

    • Search Google Scholar
    • Export Citation
  • Holling, C.S. 1959b. Some characteristics of simple types of predation and parasitism. Can. Entomol. 91:385398.

  • Jeschke, J.M., M. Kopp and R. Tollrian. 2002. Predator functional responses: discriminating between handling and digesting prey. Ecol. Monogr. 72(1):95-112.

    • Search Google Scholar
    • Export Citation
  • Juliano, S.A. 2001. Nonlinear curve fitting: predation and functional response curves. In: S.M. Scheiner and J. Gurevitch (eds), Design and Analysis of Ecological Experiments. Oxford University Press, Oxford, UK, pp. 178-196.

    • Search Google Scholar
    • Export Citation
  • Kass, R.E. and A.E. Raftery. 1995. Bayes factors. J. Am. Stat. Ass. 90: 773795.

  • Livdahl, T.P. 1979. Evolution of handling time: the functional response of a predator to the 450 density of sympatric and allopatric strains of prey. Evolution 33(2):765-768.

    • Search Google Scholar
    • Export Citation
  • Livdahl, T.P. and A.E. Stiven. 1983. Statistical difficulties in the analysis of predator functional response data. Can. Entomol. 115:13651370.

    • Search Google Scholar
    • Export Citation
  • Metropolis, N., A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller and E. Teller. 1953. Equation of state calculations by fast computing machines. J. Chem. Phys. 21:10871092.

    • Search Google Scholar
    • Export Citation
  • Okuyama, T. 2012a. Flexible components of functional responses. J. Anim. Ecol. 81:185189.

  • Okuyama, T. 2012b. A likelihood approach for functional response models. Biol. Contr. 60(2):103-107.

  • Papanikolaou, N.E., P.G. Milonas, D.C. Kontodimas, N. Demiris and Y.G. Matsinos. 2013. Temperature-dependent development, survival, longevity and fecundity of Propylea quatuordecimpunctata (Coleoptera: Coccinellidae). Ann. Entomol. Soc. Am. 106(2):228-234.

    • Search Google Scholar
    • Export Citation
  • Papanikolaou, N.E., A.F. Martinou, D.C. Kontodimas, Y.G. Matsinos and P.G. Milonas. 2011. Functional responses of immature stages of Propylea quatuordecimpunctata (Coleoptera: Coccinellidae) to Aphis fabae (Hemiptera: Aphididae). Eur. J. Entomol. 108(3):391-395.

    • Search Google Scholar
    • Export Citation
  • R Core Team . 2013. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

    • Search Google Scholar
    • Export Citation
  • Rogers, D.J. 1972. Random search and insect population models. J. Anim. Ecol. 41: 369383.

  • Sentis, A., J.L. Hemptinne and J. Brodeur. 2012. Using functional response modeling to investigate the effect of temperature on predator feeding rate and energetic efficiency. Oecologia 169(4):1117-1125.

    • Search Google Scholar
    • Export Citation
  • Solomon, M. 1949. The natural control of animal populations. J. Anim. Ecol. 18(1):1-35.

  • Trexler, J., C. McCulloch and J. Travis. 1988. How can the functional response best be determined? Oecologia 76:206214.

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Senior editors

Editor(s)-in-Chief: Podani, János

Editor(s)-in-Chief: Jordán, Ferenc

Honorary Editor(s): Orlóci, László

Editorial Board

  • Madhur Anand, CAN (forest ecology, computational ecology, and ecological complexity)
  • S. Bagella, ITA (temporal dynamics, including succession, community level patterns of species richness and diversity, experimental studies of plant, animal and microbial communities, plant communities of the Mediterranean)
  • P. Batáry, HUN (landscape ecology, agroecology, ecosystem services)
  • P. A. V. Borges, PRT (community level patterns of species richness and diversity, sampling in theory and practice)
  • A. Davis, GER (supervised learning, multitrophic interactions, food webs, multivariate analysis, ecological statistics, experimental design, fractals, parasitoids, species diversity, community assembly, ticks, biodiversity, climate change, biological networks, cranes, olfactometry, evolution)
  • Z. Elek, HUN (insect ecology, invertebrate conservation, population dynamics, especially of long-term field studies, insect sampling)
  • T. Kalapos, HUN (community level plant ecophysiology, grassland ecology, vegetation-soil relationship)
  • G. M. Kovács, HUN (microbial ecology, plant-fungus interactions, mycorrhizas)
  • W. C. Liu,TWN (community-based ecological theory and modelling issues, temporal dynamics, including succession, trophic interactions, competition, species response to the environment)
  • L. Mucina, AUS (vegetation survey, syntaxonomy, evolutionary community ecology, assembly rules, global vegetation patterns, mediterranean ecology)
  • P. Ódor, HUN (plant communities, bryophyte ecology, numerical methods)
  • F. Rigal, FRA (island biogeography, macroecology, functional diversity, arthropod ecology)
  • D. Rocchini, ITA (biodiversity, multiple scales, spatial scales, species distribution, spatial ecology, remote sensing, ecological informatics, computational ecology)
  • F. Samu, HUN (landscape ecology, biological control, generalist predators, spiders, arthropods, conservation biology, sampling methods)
  • U. Scharler, ZAF (ecological networks, food webs, estuaries, marine, mangroves, stoichiometry, temperate, subtropical)
  • D. Schmera, HUN (aquatic communities, functional diversity, ecological theory)
  • M. Scotti, GER (community-based ecological theory and modelling issues, trophic interactions, competition, species response to the environment, ecological networks)
  • B. Tóthmérész, HUN (biodiversity, soil zoology, spatial models, macroecology, ecological modeling)
  • S. Wollrab, GER (aquatic ecology, food web dynamics, plankton ecology, predator-prey interactions)

 

Advisory Board

  • S. Bartha, HUN
  • S.L. Collins, USA
  • T. Czárán, HUN
  • E. Feoli, ITA
  • N. Kenkel, CAN
  • J. Lepš, CZE
  • S. Mazzoleni, ITA
  • Cs. Moskát, HUN
  • B. Oborny, HUN
  • M.W. Palmer, USA
  • G.P. Patil, USA
  • V. de Patta Pillar, BRA
  • C. Ricotta, ITA
  • Á. Szentesi, HUN

PODANI, JÁNOS
E-mail: podani@ludens.elte.hu


JORDÁN, FERENC
E-mail: jordan.ferenc@gmail.com

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Community Ecology
Language English
Size A4
Year of
Foundation
2000
Volumes
per Year
1
Issues
per Year
2
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245
Publisher Akadémiai Kiadó
Springer Nature Switzerland AG
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
CH-6330 Cham, Switzerland Gewerbestrasse 11.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1585-8553 (Print)
ISSN 1588-2756 (Online)