Equilibrium and non-equilibrium concepts in forest genetic modelling: population- and individually-based approaches

  • Koen Kramer Alterra
  • D. C. van der Werf Alterra
Keywords: forest genetic models, population-genetic, individually-based models, equilibrium, non-equilibrium, environmental change, METAPOP, ForGEM

Abstract

The environment is changing and so are forests, in their functioning, in species composition, and in the species’ genetic composition. Many empirical and process-based models exist to support forest management. However, most of these models do not consider the impact of environmental changes and forest management on genetic diversity nor on the rate of adaptation of critical plant processes. How genetic diversity and rates of adaptation depend on management actions is a crucial next step in model development. Modelling approaches of genetic and demographic processes that operate in forests are categorized here in two classes. One approach assumes equilibrium conditions in phenotype and tree density, and analyses the characteristics of the demography and the genetic system of the species that determine the rate at which that equilibrium is attained. The other modelling approach does not assume equilibrium conditions and describes both the ecological —and genetic processes to analyse how environmental changes result in selection pressures on functional traits of trees and the consequences of that selection for tree— and ecosystem functioning. The equilibrium approach allows analysing the recovery rate after a perturbation in stable environments, i.e. towards the same pre-perturbation stable state. The nonequilibrium approach allows, in addition to the equilibrium approach, analysing consequences of ongoing environmental changes and forest management, i.e. non-stationary environments, on tree functioning, species composition, and genetic composition of the trees in forest ecosystem. In this paper we describe these two modelling approaches and discuss advantages and disadvantages of them and current knowledge gaps.
Published
2010-10-05
Section
Articulos originales