Method for Selection of Old‐Forest Reserves
Abstract: We used a multi‐objective greedy heuristic algorithm in a landscape area of 96,000 ha in northern Finland to select old‐forest stands that would best complement existing reserves in terms of achievement of conservation objectives while minimizing costs. Several quantitative nonspatial and...
Published in: | Conservation Biology |
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Main Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Wiley
2002
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Subjects: | |
Online Access: | http://dx.doi.org/10.1046/j.1523-1739.2002.00322.x https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1046%2Fj.1523-1739.2002.00322.x http://onlinelibrary.wiley.com/wol1/doi/10.1046/j.1523-1739.2002.00322.x/fullpdf |
Summary: | Abstract: We used a multi‐objective greedy heuristic algorithm in a landscape area of 96,000 ha in northern Finland to select old‐forest stands that would best complement existing reserves in terms of achievement of conservation objectives while minimizing costs. Several quantitative nonspatial and spatial objectives for quality, proportion of area, and spatial distribution of old forests were specified in cooperation with foresters and conservation planners. The cost of each stand was calculated on the basis of the economic value of forest land. The algorithm calculated cost‐benefit ratios for the stands on the basis of how well each stand satisfied the objectives and at what cost. In every cycle, the algorithm selected the stand with the best cost‐benefit ratio. The algorithm included three spatial functions—continuous area, connectivity, and isolation—that were used to determine the spatial distribution of the stands. In addition, an option for preselection of stands (e.g., existing reserves) was included. A comparison between two solutions, one with and one without spatial objectives, showed that the network of selected old‐forest stands was less fragmented and more evenly distributed with use of the spatial objectives. The use of spatial objectives did not increase costs or the area needed, and the nonspatial objectives were met in both solutions. The nonspatial and spatial objectives are defined by the user, so the algorithm can be applied to other multi‐objective problems. |
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