• The conservation planning cloud aiReserve can help you to achieve the maximum protection results with minimum costs and limited resources.
• FSOS, a world-class forest management planning model, is an ideal foundation for developing an optimal reserve selection model (http://forestcloud.ca).
• The most difficult challenges in developing world-class conservation planning cloud have already been overcome through the years of work that went into developing FSOS.
• You do not need to install any software to run aiReserve cloud, with any devices, at any time and any places.
• The aiReserve is an easy, powerful and ideal tool for reserve area selections in the fields of education, research and practical applications.
• 1992: International Convention on Biodiversity (in Rio de Janeiro)
• Important outcome from this convention:
Leaders of the majority of countries in the world agreed to place 12 per cent of their land base into conservation reserves. The percentage has been increasing continuously after that.
• Conservation planning began on a world-wide scale, and continues to this day.
Reserve Selection Models
Shortly after 1992, the Optimal Reserve Selection Model was developed.
The objective of this model, in its simplest form, is: To select a set of candidate reserves to add to an existing reserve network such that: The number of unique elements of biodiversity is maximized within the total network of reserves, with an upper bound on the total area or cost of the reserves to be added.
In short, this model finds the mathematically most efficient way to reserve land for the objective of conserving biodiversity where: an “element of biodiversity” is usually measured as a species or an ecosystem type (both terrestrial and aquatic).
The selected reserve network has spatial constraints on: the size of a patch of land within a reserve network needed for population persistence, the shape of a patch of land within a selected reserve network needed for population persistence, the proximity of a patch of land to other land uses, e.g., close proximity to non-conflicting land uses (e.g., agriculture, forestry, recreation, etc.) and distant proximity from conflicting land-uses (e.g., intensive industrial use).
When used at a county-scale, this optimization model is computationally very difficult to solve and requires: advanced artificial intelligence algorithms to generate high quality solutions.
The major input to the model: accurate distribution maps of each “element” of biodiversity.
Major output from model: efficient conservation zoning plans, covers maximum biodiversity at minimum land cost. Effective plans that integrate conservation of biodiversity with multiple land uses (parks, reserves, forestry, agriculture) and separates conserved elements of biodiversity from unsustainable proximity to conflicting land uses.
We need software to generate effective Politically Feasible Plans that assists policy-makers in discovering acceptable trade-offs between competing land-use interests.