5.6 Modelling wildlife populations using GIS

Although it is increasingly recognised that wildlife management involves managing not only individual species, but the whole ecosystem that they inhabit, the preservation of ‘flagship’ species (usually large mammals) is a major concern to the general public. The increasing size of the IUCN’s ‘red list’ of endangered species also makes efforts to preserve individual species a priority (see IUCN reference below). GIS can be used to assist in this process.

Park managers frequently have to make spatial decisions about wildlife populations. Examples of such decisions might include:

  • Where to reintroduce a new species into a given area so that the reintroduction is successful. For example, there has been some discussion about reintroducing beaver into the UK, but where should the first beaver be released?
  • Where to translocate individual animals, e.g. to ensure as many healthy sub-populations as possible, should animals of a rare species be moved from a national park to another location?
  • Whether a large-scale fire might eliminate an entire wildlife population, or whether some sub-populations would be unaffected?
  • Where to plan open ‘deer glades’ within plantation forestry, so as to encourage a buoyant local deer population.


Meta-population models

Since all of these decisions are spatial (i.e. concerned with doing something at a particular location), GIS can be used to help plan where, say, a reintroduction or a controlled burn should take place. This is often undertaken using a meta-population model linked to a GIS. A meta-population model looks at animal populations living in different habitat patches in a landscape and considers how these populations change over time. This class of model is called a meta-population model because the overall wildlife population is sub-divided into many smaller β€˜meta-populations’, each living in a separate habitat patch. One of the most widely used tools for modelling wildlife in this way is RAMAS-GIS (see below), a software package that can be linked to a GIS system to support such decisions.

A model of a given wildlife population can be developed using the RAMAS-GIS software in several stages. The early stages are normally undertaken within a GIS, whilst the later stages are undertaken using specialist modelling software. The RAMAS-GIS web site (see below) provides a very useful summary of these stages and you should read the software description on this site.

  1. In the first stage, the landscape is classified into two types of area: patches of suitable habitat for the species concerned and patches of unsuitable habitat. The animal may move through the unsuitable habitat, but will be unable to live there permanently. The suitable habitat patches are often identified using a habitat suitability index. This habitat suitability index may be based on a statistical analysis of existing species distributions or on expert ecological opinion as to what constitutes an appropriate habitat for the animal concerned.
  2. In the second stage, a carrying capacity is estimated for each habitat patch. The carrying capacity is the number of animals that it can support and is based on its area and sometimes also the habitat quality within each patch. At this stage, the distance between each suitable patch of habitat and every other suitable patch is also calculated. This distance information is used to simulate species migration later on.
  3. In the third stage, this information is exported from the GIS into meta-population modelling software. Within the modelling software, a population age-sex structure can be specified for each patch, based on ecological field data. Birth and death rates for animals can also be specified from field data. Details of the species’ propensity to migrate can also be added at this stage – for example, by bringing in records of birds that have been tagged and released in the field.
  4. In the final stage, the dynamics of the wildlife population can be modelled to see whether it remains stable, declines, or increases and spreads through a landscape. One example of a model scenario might be the release of pairs of breeding beaver into the wild. Different release points can be assessed with separate model runs to find the optimal location for releasing beaver that will maximise their chances of establishment. The model is often run over many years. In some cases, the geography of the habitat patches can be modified during the model run. For example, a simulated bush fire may be introduced in year 3, which destroys 2 of the 5 patches of suitable habitat for a given species of rodent.

This type of model is sometimes known in ecology as a spatially explicit model, since each meta-population is linked to a habitat patch in the ‘real world’.


  1. To test your understanding of meta-population models and GIS, try answering the questions in the following quiz:


    Modelling wildlife populations using GIS


  2. In general, to what extent would you trust the output from a RAMAS-GIS model? What are your reasons for thinking the software would or would not produce reliable results? Post your thoughts to the course discussion board.

References (Essential reading for this learning object indicated by *)

* You can find out more about the RAMAS-GIS software by visiting the manufacturer’s web site: www.ramas.com

The IUCN-World Conservation Union maintains a ‘red list’ of endangered species, which is available at: https://www.iucnredlist.org/

Comments are closed.