plant: A package for modelling forest trait ecology and evolution

Abstract

Summary1. Population dynamics in forests are strongly size-structured: larger plants shade smaller plants while also expending proportionately more energy on building and maintaining woody stems. Although the importance of size structure for demography is widely recognized, many models either omit it entirely or include only coarse approximations. 2. Here, we introduce the plant package, an extensible framework for modelling size-and trait-structured demography, ecology and evolution in simulated forests. At its core, plant is an individual-based model where plant physiology and demography are mediated by traits. Individual plants from multiple species can be grown in isolation, in patches of competing plants or in metapopulations under a disturbance regime. These dynamics can be integrated into metapopulation-level estimates of invasion fitness and vegetation structure. Because fitness emerges as a function of traits, plant provides a novel arena for exploring eco-evolutionary dynamics. 3. plant is an open source R package and is available at github.com/traitecoevo/plant. Accessed from R, the core routines in plant are written in C++. The package provides for alternative physiologies and for capturing trade-offs among parameters. A detailed test suite is provided to ensure correct behaviour of the code. 4. plant provides a transparent platform for investigating how physiological rules and functional trade-offs interact with competition and disturbance regimes to influence vegetation demography, structure and diversity.

Functional traits as predictors of vital rates across the life cycle of tropical trees

“…; Hérault et al . ; Falster, FitzJohn & Westoby ). Thirdly, many studies consider only a single trait, thus inherently limiting the total explanatory power of traits (Muller‐Landau et al .…”

On the performance of four methods for the numerical solution of ecologically realistic size‐structured population models

“…The problem of diverging characteristic curves can be overcome with an adaptive time schedule for their introduction (Falster et al . ), but this recently developed improvement of the CM is outside the scope of the present work. To focus on the performance of the other three numerical methods, we will not consider the CM further in this section.…”

“…However, a recent numerical implementation of a closely related vegetation model (Falster et al . ) shows that an adaptive time schedule for the introduction of characteristic curves can resolve the problem of divergence in the CM. A similar implementation of the EBT method can also overcome the problem of cohort divergence (Falster et al .…”

Using functional traits to predict species growth trajectories, and cross‐validation to evaluate these models for ecological prediction

“…Trait‐based schemes enable researchers to generalize across species. An interest in quantitatively linking traits to life history functions led to qualitative predictions of demographic rates from traits (Noble & Slatyer, ), and more recently, the consolidation of trait‐based studies has allowed for more quantitative predictions to be made (Falster, FitzJohn, et al, ). One worthy future of functional trait research is to apply trait‐based predictions to applied management problems; where resources are limited yet decisions must be made across broad suites of species.…”

“…However, before trait‐based models can be applied widely, thorough interrogation and hard tests of the predictive capacity of this approach is necessary (Visser et al ). Models of individual growth range from simple calculations of relative growth rates (Poorter, ) to complex mechanistic models (Falster, FitzJohn, Brännström, Dieckmann, & Westoby, ; Prusinkiewicz, ). Although some very large data sets support complex models (Rüger et al, ), many empirical studies generate small data sets to test novel ideas (Falster & Westoby, ) or to guide management decisions (Muir et al, ; Munro et al, ).…”