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New insights on the behaviour of alternative types of individual-based tree models for natural forests

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  • Häbel, Henrike
  • Myllymäki, Mari
  • Pommerening, Arne

Abstract

Agent/individual-based models (A/IBM) help to explain in a mechanistic way how spatial plant patterns evolve through time. In the past, seemingly different and independent types of A/IBMs were developed for modelling the dynamics of tree populations, e.g. growth interaction (GI) and shot noise (SN) models. In this paper, we present a new, advanced methodology of pattern-oriented modelling (POM) for the comparative, synoptic analysis of the behaviour of different types of A/IBMs by using recombinations of model components, validation and sensitivity analysis. We analysed model behaviour for spatio-temporal data from natural forests of interior Douglas fir (Pseudotsuga menziesii var glauca (Mirb.) Franco) and Scots pine (Pinus sylvestris L.) populations from Canada and the UK, respectively. Our detailed analysis clarified that both models, GI and SN along with their recombinations performed similarly and belong to the same group of A/IBMs. From the application of our new methodology we learnt that SN models were able to describe interactions more accurately than GI models and additionally produce interaction fields that can be used for other modelling purposes. On the other hand the GI model was more robust when using observed data that did not include sufficient information on tree interactions. Maximum-likelihood estimations were more reliable in spatial regression analysis than least-squares methods and should be preferred in spatial A/IBM parametrisation.

Suggested Citation

  • Häbel, Henrike & Myllymäki, Mari & Pommerening, Arne, 2019. "New insights on the behaviour of alternative types of individual-based tree models for natural forests," Ecological Modelling, Elsevier, vol. 406(C), pages 23-32.
    • Handle: RePEc:eee:ecomod:v:406:y:2019:i:c:p:23-32
    DOI: 10.1016/j.ecolmodel.2019.02.013
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    References listed on IDEAS

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    1. Fibich, Pavel & Lepš, Jan, 2011. "Do biodiversity indices behave as expected from traits of constituent species in simulated scenarios?," Ecological Modelling, Elsevier, vol. 222(13), pages 2049-2058.
    2. Pommerening, Arne & LeMay, Valerie & Stoyan, Dietrich, 2011. "Model-based analysis of the influence of ecological processes on forest point pattern formation—A case study," Ecological Modelling, Elsevier, vol. 222(3), pages 666-678.
    3. Pommerening, Arne & Särkkä, Aila, 2013. "What mark variograms tell about spatial plant interactions," Ecological Modelling, Elsevier, vol. 251(C), pages 64-72.
    4. Renshaw, Eric & Sarkka, Aila, 2001. "Gibbs point processes for studying the development of spatial-temporal stochastic processes," Computational Statistics & Data Analysis, Elsevier, vol. 36(1), pages 85-105, March.
    5. Saloranta, Tuomo M. & Andersen, Tom, 2007. "MyLake—A multi-year lake simulation model code suitable for uncertainty and sensitivity analysis simulations," Ecological Modelling, Elsevier, vol. 207(1), pages 45-60.
    6. Pommerening, Arne & Muszta, Anders, 2016. "Relative plant growth revisited: Towards a mathematical standardisation of separate approaches," Ecological Modelling, Elsevier, vol. 320(C), pages 383-392.
    7. Sarkka, Aila & Renshaw, Eric, 2006. "The analysis of marked point patterns evolving through space and time," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1698-1718, December.
    8. Redenbach, Claudia & Särkkä, Aila, 2013. "Parameter estimation for growth interaction processes using spatio-temporal information," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 672-683.
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    Cited by:

    1. Frédéric Lavancier & Ronan Le Guével, 2021. "Spatial birth–death–move processes: Basic properties and estimation of their intensity functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 798-825, September.
    2. Pommerening, Arne & Sterba, Hubert & West, Philip, 2022. "Sampling theory inspires quantitative forest ecology: The story of the relascope kernel function," Ecological Modelling, Elsevier, vol. 467(C).
    3. Myllymäki, Mari & Kuronen, Mikko & Bianchi, Simone & Pommerening, Arne & Mehtätalo, Lauri, 2024. "A Bayesian approach to projecting forest dynamics and related uncertainty: An application to continuous cover forests," Ecological Modelling, Elsevier, vol. 491(C).

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