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Modelling assumptions and limitations

All computer models must make some assumptions to do their job. This leads to certain limitations.

Focal species

The dispersal ability of different species varies. Some species may cover long distances to disperse, but others can move only a few metres. A habitat network that works for one species may not be ideal for another. So models often use a ‘focal species’ approach.

Focal species are used to represent the typical characteristics of a group of species. For example, the corn bunting has been used to represent farmland birds in the Forest Research Lowland habitat network project. Network models are then based on the habitat area requirement and dispersal ability of the focal species.

But such information isn’t available for many species, and so assumptions and estimates must be made, which may vary in accuracy. Also, typical characteristics serve as an average for that species – any single individual plant or animal may behave very differently in reality.

Sometimes there isn’t a single species that can be used in the design of a habitat network. A ‘generic focal species’ may be used instead. This is a theoretical creation that represents the characteristics of a range of species.

An example of a generic focal species is an ‘ancient woodland specialist with high area requirements and limited dispersal ability’. Assumptions will be made about how it behaves for use in the modelling process. But these may not reflect the actual behaviour of the various species that the model is meant to represent.

Data quality

Data quality can be a significant limitation. A model is only as good as the data used to construct it, and the questions you ask it to answer.

But there isn’t always enough real-world data available for habitat network models to work effectively. For example, you may have only limited data on species characteristics or about the quality of the existing habitat.

If a model is built with very limited habitat data, you might assume there aren’t any patches of good habitat when there are actually several areas of excellent habitat. Many of the following calculations made by the model will then be inaccurate.

When selecting data for modelling, consider the:

  • question you wish to answer – what is it that you’re trying to model?
  • scale on which you wish to model
  • accuracy of the data you hold

Scale of focus

You must also think about the scale at which you’re assessing habitat networks.

Usually, the smaller the scale on which you wish to focus, the more detailed the data you’ll need. But the only suitably detailed habitat data – e.g. Phase 1 habitat surveys – may be outdated.

If you’re looking at habitat connectivity for a common habitat over a large scale, land cover data sets at a fairly coarse resolution – e.g. those within Ordnance Survey MasterMap – may be enough.