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1. Conservation management often encompasses multiple, alternative management actions on a given site, involving habitat restoration and maintenance for example. Which actions are preferable depends on the conservation goals, the expected outcomes of actions, and their associated costs. When actions affect habitat quality differently, species that vary in habitat requirements will not respond to the actions in the same way. When all these species are of conservation concern, trade-offs between them are inevitable and the selection of appropriate actions becomes less straightforward. Although this is a common planning problem, it has received little attention in the conservation planning literature. 2. We demonstrate how to obtain cost-effective planning solutions for a set of species with contrasting requirements, when multiple alternative conservation actions are available for each site. We investigate the strength of trade-offs between species with different habitat preferences, when planning optimal management of a set of semi-natural mesic grassland sites. A community of vascular plants and Lepidoptera species depends on such grasslands, which are maintained by cattle grazing. The various species differ in their responses to grazing intensity. We apply an algorithm that selects a grazing intensity for each site to maximize the benefit over all species, under a given budget constraint. 3. The optimal grazing intensity for sites, and consequently, the expected representation of species, was sensitive to the relative values assigned to species (weights) and the budget available. The outcome also depended on assumptions regarding species representation under suboptimal management. A sensitivity analysis showed that the trade-offs between species were strong, illustrating the potentially significant consequences of conservation decisions. 4. Synthesis and applications. Maximizing conservation benefit over all species may result in high representation of some species at the cost of others. Although this is a natural consequence of budget limitations and conservation priorities, understanding these consequences is essential if planning is to accommodate species with different conservation needs. Our methodology is a novel extension of conventional reserve selection methods and can support planning for species with contrasting requirements, leading to more robust and cost-effective conservation decisions. [ABSTRACT FROM AUTHOR]

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]

The limited availability of resources for conservation has led to the development of many quantitative methods for selecting reserves that aim to maximize the biodiversity value of reserve networks. In published analyses, species are often considered equal, although some are in much greater need of protection than others. Furthermore, representation is usually treated as a threshold: a species is either represented or not, but varying levels of representation over or under a given target level are not valued differently. We propose that a higher representation level should also have higher value. We introduce a framework for reserve selection that includes species weights and benefit functions for under- and overrepresentation (number of locations for each species). We applied the method to conservation planning for herb-rich forests in southern Finland. Our use of benefit functions and weighting changed the identity of about 50% of the selected sites at different funding levels and improved the representation of rare and threatened species. We also identified a small area of additional land that would substantially enhance the existing reserve network. We suggest that benefit functions and species weighting should be considered as standard options in reserve-selection applications. [ABSTRACT FROM AUTHOR]

Reserve selection often concerns the design of reserve networks for the long-term maintenance of biodiversity. We considered uncertainty in the context of three common reserve-selection formulations, the expected number of populations, proportional coverage of land-cover types, and the probability of having at least one population. By uncertainty, we mean variance in the outcome of any probability-based reserve selection formulation. A typical reserve-selection formulation might ask for the least expensive set of sites that contains n populations per species. It is implicit here that this requirement concerns the expected number of populations, which actually is obtained only with a 50% chance. If the requirement is changed to select the least expensive set of sites that gives n populations per species with a 95% probability, the number of sites required in the solution increases and the identity of the sites is changed toward sites that have high probabilities of persistence (or occurrence) and low associated binomial variance. Anthropogenic threat is one factor that may cause probabilistic uncertainty in the context of proportional area coverage. [ABSTRACT FROM AUTHOR]