Model Types for Ecological Modelling
Sven Erik Jørgensen introduces a recent issue of Ecological Modelling that presents selected papers from the International Conference on Ecological Modelling in Yamaguchi, Japan (28 August - 1 September 2006). The paper provides an overview of the model types available for ecological modelling, briefly highlighting the shift from a dominance of bio-geo-chemical dynamic models and population dynamics models in the 1970s toward the application of a wider spectrum of models. The emergence of new model types has come as a response to questions such as:
Jørgensen offers a short description of each type, before listing their advantages and disadvantages. Here are a couple with my comments in italics:
Individual-Based Models (IBMs)and Cellular Automata (CA)
First, counter to Jørgensen, I would argue that CA models should be placed with the 'spatial models' - the ability of CA to represent space for me outweighs their potential to represent (limited) heterogeneity between cells. This aside, their grouping does make sense when we consider that these models can be relatively easily combined to represent individuals' interactions across space and with a heterogeneous environment (via the CA).
Advantages
Advantages
- How can we describe the spatial distribution which is often crucial to understand ecosystem reactions?
- How do we model middle number systems?
- How do we model hetergenous populations and databases (e.g. observations from many different ecosystems)?
- How do we model ecosystems, when our knowledge is mainly based on a number of rules/properties/propositions?
- (Bio-geo-chemical and bio-energetics), dynamic models
- Static models
- Population dynamic models
- Structurally dynamic models
- Fuzzy models
- Artificial neural networks
- Individual-based models and cellular automata
- Spatial models
- Ecotoxicological models
- Stochastic models
- Hybrid models
Jørgensen offers a short description of each type, before listing their advantages and disadvantages. Here are a couple with my comments in italics:
Individual-Based Models (IBMs)and Cellular Automata (CA)
First, counter to Jørgensen, I would argue that CA models should be placed with the 'spatial models' - the ability of CA to represent space for me outweighs their potential to represent (limited) heterogeneity between cells. This aside, their grouping does make sense when we consider that these models can be relatively easily combined to represent individuals' interactions across space and with a heterogeneous environment (via the CA).
Advantages
- Are able to account for individuality - agreed, especially for IBMs
- Are able to account for adaptation within the spectrum of properties - yes
- Software is available; although the choice is more limited than by bio-geo-chemical dynamic models - but excellent free modelling environments such as NetLogo make this type of modelling widely available
- Spatial distribution can be covered - yes
- If many properties are considered, the models get very complex - and may require the adoption and development of new techniques to present/analyse/interpret output (e.g. POM, narratives)
- Can be used to cover the individuality of populations; but they cannot cover mass and energy transfer based on the conservation principle - I see no reason why the principle of energy and mass conservation could not be achieved by models of these types
- Require many data to calibrate and validate the models - yes, this often the case, and in some cases (again) may require new approaches and types of data to calibrate and evaluate models
Advantages
- Cover spatial distribution, that is often of importance in ecology - yes, particularly Landscape Ecology, an entire discipline that has arisen since the 1970s and '80s
- The results can be presented in many informative ways, for instance GIS - GIS is a means to organise and analyse data as well as present data
- Require usually a huge database, giving information about the spatial distribution - this can certainly give rise to the issue of 'model but no data' and increases the costs of performing ecological research by adding space to time. We have found that our large (~4,000 sq km) Upper Michigan study area demands high time and resources needed for data collection.
- Calibration and validation are difficult and time-consuming - maybe more so than non-spatial models, but probably not as much as some individual-based models
- A very complex model is usually needed to give a proper description of the spatial patterns - not necessarily. A model should be only as complex as the patterns and processes it seeks to examine and the inclusion of space does not imply patterns or processes any more complex than a system with less variables or interactions that is non-spatial.
Labels: Academic, Ecological, Environmental, Modelling
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
2 Comments:
I read that article, and thought the breakdown of model types was strange. It is sort of a mixed bag. I would have divided them by modeling approach: matematical, system dynamics, agent based, spatial, etc. He has some categorized by subject and some by modeling approach. I also didn't agree with the strengths and weaknesses of some.
Cheers, Richard
I agree. This breakdown is quite messy and illogical ("Ecotoxicological models"...).
@Richard G. Dudley: what you propose (mathematical, SD, agent-based, spatial, etc) is not much better in my humble opinion. Agent-based and SD especially are not to be opposed to "spatial".
I guess the breakdown could be done depending on the descriptive language and technique (SD, Agent-based, neural network, mathematical, etc) whereas "spatial" just refers to the number of dimensions which can be tackled. This last aspect is quite independent from the approach chosen (even if some are inherently spatially explicit whereas others aren't).
Post a Comment
<< Home