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Aggregation of reserve networks is generally considered desirable for biological and economic reasons: aggregation reduces negative edge effects and facilitates metapopulation dynamics, which plausibly leads to improved persistence of species. Economically, aggregated networks are less expensive to manage than fragmented ones. Therefore, many reserve-design methods use qualitative heuristics, such as distance-based criteria or boundary-length penalties to induce reserve aggregation. We devised a quantitative method that introduces aggregation into reserve networks. We call the method the boundary-quality penalty (BQP) because the biological value of a land unit (grid cell) is penalized when the unit occurs close enough to the edge of a reserve such that a fragmentation or edge effect would reduce population densities in the reserved cell. The BQP can be estimated for any habitat model that includes neighborhood (connectivity) effects, and it can be introduced into reserve selection software in a standardized manner. We used the BQP in a reserve-design case study of the Hunter Valley of southeastern Australia. The BQP resulted in a more highly aggregated reserve network structure. The degree of aggregation required was specified by observed (albeit modeled) biological responses to fragmentation. Estimating the effects of fragmentation on individual species and incorporating estimated effects in the objective function of reserve-selection algorithms is a coherent and defensible way to select aggregated reserves. We implemented the BQP in the context of the Zonation method, but it could as well be implemented into any other spatially explicit reserve-planning framework. [ABSTRACT FROM AUTHOR]

Methods for reserve selection and conservation planning often ignore uncertainty. For example, presence-absence observations and predictions of habitat models are used as inputs but commonly assumed to be without error. We applied information-gap decision theory to develop uncertainty analysis methods for reserve selection. Our proposed method seeks a solution that is robust in achieving a given conservation target, despite uncertainty in the data. We maximized robustness in reserve selection through a novel method, “distribution discounting,” in which the site- and species-specific measure of conservation value (related to species-specific occupancy probabilities) was penalized by an error measure (in our study, related to accuracy of statistical prediction). Because distribution discounting can be implemented as a modification of input files, it is a computationally efficient solution for implementing uncertainty analysis into reserve selection. Thus, the method is particularly useful for high-dimensional decision problems characteristic of regional conservation assessment. We implemented distribution discounting in the zonation reserve-selection algorithm that produces a hierarchy of conservation priorities throughout the landscape. We applied it to reserve selection for seven priority fauna in a landscape in New South Wales, Australia. The distribution discounting method can be easily adapted for use with different kinds of data (e.g., probability of occurrence or abundance) and different landscape descriptions (grid or patch based) and incorporated into other reserve-selection algorithms and software. [ABSTRACT FROM AUTHOR]