Forest Landscape Models: A Review

There’s a new forest landscape model classification and review out there, recently published in Forest Ecology and Management by Hong He. The paper assumes greater familiarity with the topic of forest and disturbance modelling than the paper I recently published with my former advisor, George Perry, and discussion focuses largely on models primarily developed for the study of temperate forest systems in the USA (e.g., JABOWA, SORTIE, LANDIS, ZELIG – exceptions include MAQUIS and FORMOSAIC).


Distinction between deterministic models and stochastic models

He suggests that, generally, ecological models fall in two seemingly exclusive categories, deterministic models and stochastic models, and that either category of model can use physical or empirical approaches, or a combination of both (see figure). However, the classification He presents in the paper is developed according to how models represent

  1. spatial processes,
  2. temporal processes,
  3. site-level succession, and
  4. the intended use of the model.

Models are classified by succession based on whether the model uses succession pathways (i.e., a Markov state-and-transition approach), vital attributes (as I utilised in my PhD modelling), or by coupling landscape models with more detailed stand-level vegetation succession models. The fourth classification criteria above highlights that there are numerous applications of forest landscape models, and that design is strongly related to the desired applications. He suggests applications of forest landscape models generally fall into one of three categories:

  1. spatiotemporal patterns of model objects,
  2. sensitivities of model object to input parameters, and
  3. comparisons of model simulation scenarios.

After developing and presenting the classification, the paper goes on to discuss two dilemmas facing those using forest landscape models. The first is the validation of model results, which has been discussed on numerous occasion elsewhere (including this blog). The discussion on circular reasoning is more novel however, (and related in some ways to what I have written with regards models of human agents):

“It is often difficult to separate expected results from emergent results. A caution against circular reasoning is the caveat often encountered in this situation, where researchers discuss biological or environmental forcing (causes) of their modeled results, whereas the forcing (causes) is actually built in the model formulation to derive such results. It should be pointed out that most model simulations do not lead to new understanding of the modeled processes themselves. The primary and subsequent results simply reflect the relationships used in building the models, which in turn reflect current understanding of the processes. The findings of these models are simply the spatiotemporal variations of the spatial process (discussed in Section 5.1), not the mechanisms that drive the potential changes of the spatial process. Emergent results are generally those resulted from the interactions and feedbacks of model objects.”


The paper concludes by summarizing likely development of forest landscape modelling in the future:

  1. Model development will move from the foci of theoretical and exploratory purposes to the foci of strategic and tactical purposes with increasing model realism, responding to the needs of forest management and planning.
  2. Multiple spatial and temporal resolutions will be implemented for different processes
  3. Standardized module components may emerge as handy utilities that are ready to be plugged into other models. Since component-based models provide non-developers or end users with access to model components, a component-based model can be more rigorously tested, evaluated, and modified than before, and thus, model development processes can be driven not solely by original developers, but by the broader scientific community
  4. Synchronization of multiple ecological processes can be made possible with multiple computer processors. This will help deal with the limitation that ecological processes are simulated in a sequential order as determined by the executable program.
  5. Model memorization will be improved so that a forest landscape model not only memorizes vegetation, disturbance, and management status at the current and previous model iteration, but also the entire temporal sequence. This would allow more effective studies of legacies of forested landscapes responding to various disturbance and management activities.


Here’s the full paper citation and abstract:

He (2008) Forest landscape models: Definitions, characterization, and classification Forest Ecology and Management 254 (3) Pages 484-498

Abstract
Previous model classification efforts have led to a broad group of models from site-scale (non-spatial) gap models to continental-scale biogeographical models due to a lack of definition of landscape models. Such classifications become inefficient to compare approaches and techniques that are specifically associated with forest landscape modeling. This paper provides definitions of key terminologies commonly used in forest landscape modeling to classify forest landscape models. It presents a set of qualitative criteria for model classification. These criteria represent model definitions and key model implementation decisions, including the temporal resolution, number of spatial processes simulated, and approaches to simulate site-level succession. Four approaches of simulating site level succession are summarized: (1) no site-level succession (spatial processes as surrogates), (2) successional pathway, (3) vital attribute, and (4) model coupling. Computational load for the first three approaches is calculated using the Big O Notation, a standard method. Classification criteria are organized in a hierarchical order that creates a dichotomous tree with each end node representing a group of models with similar traits. The classified models fall into various groups ranging from theoretical and empirical to strategic and tactical. The paper summarizes the applications of forest landscape models into three categories: (1) spatiotemporal patterns of model objects, (2) sensitivities of model object to input parameters, and (3) scenario analyses. Finally, the paper discusses two dilemmas related to the use of forest landscape models: result validation and circular reasoning.

Keywords Forest landscape models; Spatially explicit; Spatially interactive; Definitions; Model characterization; Model classification

One thought on “Forest Landscape Models: A Review

  1. this is great! I’m so glad I found your blog. I was searching for landscape-scale models and Google led me here. I am a research forester with the FS, working on a PhD, and foolishly thought I could pick up on landscape models (LANDIS-II) for a quick dissertation chapter. Did I underestimate the depth.

    Like

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