The Development and Application of Nebraska’s Northeast Habitat Management Decision Support System

In the face of the compounded effects of a changing climate, exponential human population growth, and accelerated land-use change, implementation of effective conservation measures grows ever more challenging, as conservation organizations are often faced with limited funding and resources. Given the complexity of managing for multiple species, each inhabiting various local habitat types, it is astonishing that we can pinpoint specific areas and tracts of land within the focus area where conservation measures would benefit all species (Figure 3). Here we demonstrate the usefulness of decision support systems in aiding policymakers and habitat managers in making decisions about where to implement conservation programs, which may be particularly beneficial when federally endangered and at-risk species are in the mix. Although many tools have recently been developed to pinpoint where suitable habitat or species occur (Donald et al. 2002; Peterson 2003; Thomas et al. 2004; Hannah & Phillips 2004; Ficetola et al. 2007; McRae and Beier 2007; Niemuth et al. 2007; Franklin 2009), few studies address the benefits of using multiple spatially explicit models to help guide conservation decisions. Within the northeastern conservation focus region in Nebraska, conservation practitioners can use the contemporary DSS to detect regions where grassland conservation could benefit many priority species, in addition to the scenario-based DSS model, which can help identify land tracts best suited for future grassland conservation programs. Though it is sometimes challenging to achieve multiple conservation objectives, particularly when multiple stakeholders are involved, the weighting system defined in the DSS criteria can be altered according to priorities or specific regions. For example, if a stakeholder group decided that Ring-necked Pheasants were a priority management concern, the group could adjust the weights within the scenario-based DSS. So, if pheasants accounted for 85% of the total weight in the DSS criteria, the resulting spatially explicit model would be highly weighted in favor of pheasant management and could help pinpoint tracts of land that have the highest likelihood of increasing pheasant populations, based on the surrounding landscape (Figure 5). Although pheasants may not be a conservation concern, this example can also be applied to Tier I at-risk species in Nebraska (Schneider et al. 2011), such as the American burying beetle. Furthermore, having the ability to rank priorities as a stakeholder group can help facilitate the structured decision-making process and achieve an outcome on which all parties can agree