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:

• 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?

Jørgensen suggests there are at least 10 types of model currently available for modelling ecological systems (purely mathematical and statistical aside):

1. (Bio-geo-chemical and bio-energetics), dynamic models


2. Static models


3. Population dynamic models


4. Structurally dynamic models


5. Fuzzy models


6. Artificial neural networks


7. Individual-based models and cellular automata


8. Spatial models


9. Ecotoxicological models


10. Stochastic models


11. Hybrid models

Of these, my particular interest is in spatial models, individual-based models and cellular automata models (with a passing interest in population models). This is largely because of my background in geography and landscape ecology, but also because of the heterogeneity in patterns, processes and behaviour often exhibited in socio-ecological systems.

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

Disadvantages

• 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

Spatial 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

Disadvantages

• 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.

This isn't a bad review of the types of ecological modelling being done. However, more incisive and useful insight could have been made with respect to landscape ecology and those models that are now beginning to attempt to account for human activity in ecological systems. [And it definitely could have been better written.] Maybe I'll stop criticising sometime and write one myself eh?