Identify priority conservation areas using habitat suitability models

Select 3-4 native animal species that best represent the landscapes and embody the specific ecosystems in need of protection/management. Identifying target species helps ECF create an approach to wildlife conservation that is easy for locals to understand - linking a charismatic species directly to landscape management practices - and helps provide locals with a tangible connection between their day-to-day conservation efforts and long-term landscape impacts. The presence of these key native animal species is later used as an indicator of biodiversity when Conservation Agreements are created.

Using a combination of remote sensing and field data, a study of the existing and potential habitats of key species is performed. Using Maximum Entropy Modeling (MAXENT) software, habitat suitability models for each key species is created, resulting in maps showing the suitability of the habitats for key species. This approach allows locals to make a clear connection between conservation objectives, measures to be implemented and the expected impacts and helps set priorities for further studies and monitor the species/habitats.

1. Access to current and accurate remote sensing landscape data – ESRI, USGA NOAA etc. 

2. Trained and educated staff to use GIS and run modelling software 

3. Combination of local and specialist data and knowledge on key species

4. Access to field data from NGOs who presently/previously worked in the region

  • Habitat suitability modelling offers a cost and time effective method to set geographic and thematic conservation priorities within a complex landscape.
  • Even with limited availability of field observation data, the results are useful in the initial stages of planning, although the limitations of the quality of the input data needs to be kept in mind.
  • Habitat suitability maps represent a good basis for discussion of conservation objectives, priorities and measures with various stakeholders, including the local population.