Nature Reserve Site Selection to Maximize Expected Species Covered

Abstract

We analyze the problem of maximizing the expected number of species in a nature reserve network, subject to a constraint on the number of sites in the network, given probabilistic information about species occurrences. The problem is a nonlinear binary integer program that is NP-hard. We develop a linear integer programming approximation that may be solved with standard integer programming software. We compare the approximation with two other approaches, an expected greedy approach and a probability hurdle approach, using probabilistic data on occurrences of terrestrial vertebrates in the state of Oregon. Results of the approximation and an exact algorithm are compared by using samples from the North American Breeding Bird Survey.

Large Scale Marine Protected Areas for Biodiversity Conservation Along a Linear Gradient: Cooperation, Strategic Behavior or Conservation Autarky?

“…1 This dissimilarity can be measured by the number of species (Weikard 2002). As we can never be sure that species are fully protected from extinction, we choose to maximize the expected total number of species, as in Polasky et al (2000), Camm et al (2002) and Arthur et al (2004). An important issue when maximizing the expected number of species is that the survival probability of a species may be dependent on the survival probability of other species (Mainwaring 2001;van der Heide et al 2005).…”

“…Maximizing the number of species subject to a budget constraint is equal to the Maximum Species Coverage Problem (Church et al 1996;Ando et al 1998;Polasky et al 2000Polasky et al , 2001Camm et al 2002;Arthur et al 2004). Minimizing the number of reserves subject to a biodiversity constraint equals the Minimum Set Cover Problem (Williams and Araéjo 2000;Sala et al 2002;Stewart et al 2003;Cabeza et al 2004;Richardson et al 2006).…”

Methodological considerations in reserve system selection: A case study of Malagasy lemurs

“…Species can be prioritized according to their IUCN Red List Categories, as in this study, or according to a wide range of other criteria, such as whether a species is a charismatic species that generates tourism revenue, a keystone species (Caro and Doherty, 1999), a provider of ecosystem services (Chan and Daily, 2008), or is more likely to provide potential for future evolution (Forest et al, 2007). Many studies have discussed the potential uses of species-specific targets or weighting systems in conservation planning algorithms in order to prioritize threatened species (Church et al, 1996;Arthur et al, 2002;Camm et al, 2002;Onal, 2004), but few have explored the effects of different targets or weighting schemes. Our different species-specific weighting schemes are designed to explore the potential effects of the common practice of preferentially weighting species that have already suffered population reductions from habitat destruction or exploitation.…”

“…It is increasingly common for species representation targets or preferential weighting schemes to be assigned to threatened or endemic species to prioritize selection of the areas where those species occur (Church et al, 1996;Arthur et al, 2002;Camm et al, 2002;Onal, 2004;Arponen et al, 2005;Kremen et al, 2008) and to maximize the proportions of their distributions represented within a reserve network (Margules and Usher, 1981;Prendergast et al, 1993;Virolainen et al, 1999). However, when threatened species are given preferential targets, or weights, a trade-off occurs such that smaller proportions of non-threatened species ranges may be included in the resulting solutions.…”

Connections in Networks: Hardness of Feasibility Versus Optimality

“…[1], [3]). In many parts of the world, land development has resulted in a reduction and fragmentation of natural habitat.…”