Biodiversity Science ›› 2011, Vol. 19 ›› Issue (4): 404-413.doi: 10.3724/SP.J.1003.2011.10020
• Special Issue •
Habitat fragmentation is one of the most important causes of biodiversity loss. In order to maximize a nature reserve’s effectiveness it is important to minimize habitat fragmentation during the design phase. However, due to economic or geographic constraints, it is often infeasible to acquire a large area of contiguous land for a reserve; designing a nature reserve consisting of several smaller components is often a more realistic choice. Selecting these smaller components with which to assemble a reserve with minimal fragmentation then becomes an important way to reduce fragmentation. How-ever, reserve site selection models which incorporate spatial attributes may encounter computational difficulties. Williams (2002) presented a linear integer programming model based on primal and dual graph concepts to select contiguous sites. This paper modified this model to design a nature reserve which can protect a set of target species while incurring minimal fragmentation among selected sites. The modified model defines two variables for each site, and the difference in the value of these two variables represents the extent of fragmentation. Computational performance tests showed that the model can solve a minimal fragmentation reserve design problem involving 100 potential sites in a rea-sonable period of time. As an empirical application, the model was employed to design reserve networks for the endangered and threatened bird species of Illinois, USA. Several reserve networks with minimal fragmentation under different scenarios were designed. The computational efficiency of linear integer programming models needs more improvement for designing large-size optimal nature reserves. The practical use of these models requires complete and accurate data sets including species distribution, cost of site selection, etc.
Yicheng Wang. A model for designing nature reserves with minimal fragmenta-tion using a primal-dual graph approach. (2011) Biodiv Sci, 19(4), 404-413.
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