Simulated annealing is often used to solve problems involving the assignment of a set of land-use activities to parts of a landscape. These include problems such as the identification of conservation reserves, where multiple criteria might be used to identify the best set (size and location) of the potential reserve areas. Another application involves the scheduling of vegetation treatments to create a desirable landscape pattern over time, such as the creation of cover and forage habitat within close proximity to develop the best habitat for ungulates. Infrastructure, such as roads and powerlines, can also be located on the landscape such that the impact on critical habitat is minimized. Particularly suitable are applications which seek efficient solutions that minimize fragmentation by controlling the size of early or late seral stages of forest cover. Characteristic of this type of problem are landscapes with many small parcels, now routinely recorded through global positioning systems and commonly stored in geographic information systems. The solution of the problem involves assigning activities to individual parcels or groups of parcels according to goals that are influenced by both space (adjacency and proximity) and time. In these and other cases, in order to control the spatial arrangement of activities, integer variables (binary) are required. The large number of binary variables often ranges between 100 000 and over 1 million, making exact solution procedures infeasible.
Assuming resources for the development of conservation reserves are limited, it may be important in some areas of the world to devote some attention to the development of a conservation plan where a large number of species (and ecosystems) of interest are maintained in an efficient reserve network. Planning for ecological goals such as these requires locating the set of potential reserve sites that address the highest priorities for conservation. Most reserve selection models utilize species presence or absence records to assist in the development of conservation scenarios. Spatial data that would benefit the modeling process include site-specific habitat condition and its position on the landscape. However, when examining a broad area, such as several million hectares, some aggregation of site-level spatial data generally occurs. Projections of future conditions may also inform the land management, development, or regulation processes, particularly where land-use change is relatively rapid and transitioning to an urbanized state.
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