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A response by C. Kremen et al. to a letter to the editor about their article "Aligning Conservation Priorities Across Taxa in Madagascar With High-Resolution Planning Tools," in the April 11, 2008 issue of "Science," is presented.

Biodiversity offsetting is the practice of using conservation actions, such as habitat restoration, management, or protection, to compensate for ecological losses caused by development activity, including construction projects. The typical goal of offsetting is no net loss (NNL), which means that all ecological losses are compensated for by commensurate offset gains. We focused on a conceptual and methodological exploration of net positive impact (NPI), an ambitious goal that implies commitment beyond NNL and that has recently received increasing attention from big business and environmental nongovernmental organizations. We identified 3 main ways NPI could be delivered: use of an additional NPI multiplier; use of slowly developing permanent offsets to deliver additional gains after NNL has first been reached during a shorter offset evaluation time interval; and the combination of permanent offsets with partially temporary losses. An important and novel variant of the last mechanism is the use of an alternate mitigation hierarchy so that gains from the traditional third step of the mitigation hierarchy (i.e., onsite rehabilitation) are no longer be counted toward reduced offset requirements. The outcome from these 3 factors is that for the same ecological damage, larger offsets will be required than previously, thereby improving offset success. As a corollary, we show that offsets are NNL only at 1 ephemeral point in time, before which they are net negative and after which they become either NPI or net negative impact, depending on whether permanent offsets are combined with partially temporary losses or if temporary offset gains are combined with partially permanent losses. To achieve NPI, offsets must be made permanent, and they must achieve NNL during an agreed-upon offset evaluation period. An additional NPI-multiplier and use of the modified mitigation hierarchy will deliver additional NPI gains. Achieving NPI is fully conditional on prior achievement of NNL, and NNL offsets have been frequently observed to fail due to inadequate policy requirements, poor planning, or incomplete implementation. Nevertheless, achieving NPI becomes straightforward if NNL can be credibly reached first., (© 2020 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)

The frequently discussed gap between conservation science and practice is manifest in the gap between spatial conservation prioritization plans and their implementation. We analyzed the research-implementation gap of one zoning case by comparing results of a spatial prioritization analysis aimed at avoiding ecological impact of peat mining in a regional zoning process with the final zoning plan. We examined the relatively complex planning process to determine the gaps among research, zoning, and decision making. We quantified the ecological costs of the differing trade-offs between ecological and socioeconomic factors included in the different zoning suggestions by comparing the landscape-level loss of ecological features (species occurrences, habitat area, etc.) between the different solutions for spatial allocation of peat mining. We also discussed with the scientists and planners the reasons for differing zoning suggestions. The implemented plan differed from the scientists suggestion in that its focus was individual ecological features rather than all the ecological features for which there were data; planners and decision makers considered effects of peat mining on areas not included in the prioritization analysis; zoning was not truly seen as a resource-allocation process and not emphasized in general minimizing ecological losses while satisfying economic needs (peat-mining potential); and decision makers based their prioritization of sites on site-level information showing high ecological value and on single legislative factors instead of finding a cost-effective landscape-level solution. We believe that if the zoning and decision-making processes are very complex, then the usefulness of science-based prioritization tools is likely to be reduced. Nevertheless, we found that high-end tools were useful in clearly exposing trade-offs between conservation and resource utilization., (© 2017 Society for Conservation Biology.)

Context: Spatial conservation prioritization (SCP) concerns, for example, identification of spatial priorities for biodiversity conservation or for impact avoidance in economic development. Software useable for SCP include Marxan, C-Plan and Zonation. SCP is often based on data about the distributions of biodiversity features (e.g., species, habitats), costs, threats, and/or ecosystem services (ES)., Objectives and Methods: At simplest ES can be entered into a SCP analysis as independent supply maps, but this is not very satisfactory because connectivity requirements and consequent ideal spatial priority patterns may vary between ES. Therefore, we examine different ES and their connectivity requirements at the conceptual level., Results: We find that the ideal spatial priority pattern for ES may differ in terms of: local supply area size and regional network requirements for the maintenance of ES provision, for flow between provision and demand, and with respect to the degree of dispersion that is needed for ES provision and access across different administrative regions. We then identify existing technical options in the Zonation software for dealing with such connectivity requirements of ES in SCP., Conclusions: This work helps users of SCP to improve how ES are accounted for in analysis together with biodiversity and other considerations., (© The Author(s) 2016.)

There is high-level political support for the use of green infrastructure (GI) across Europe, to maintain viable populations and to provide ecosystem services (ES). Even though GI is inherently a spatial concept, the modern tools for spatial planning have not been recognized, such as in the recent European Environment Agency (EEA) report. We outline a toolbox of methods useful for GI design that explicitly accounts for biodiversity and ES. Data on species occurrence, habitats, and environmental variables are increasingly available via open-access internet platforms. Such data can be synthesized by statistical species distribution modeling, producing maps of biodiversity features. These, together with maps of ES, can form the basis for GI design. We argue that spatial conservation prioritization (SCP) methods are effective tools for GI design, as the overall SCP goal is cost-effective allocation of conservation efforts. Corridors are currently promoted by the EEA as the means for implementing GI design, but they typically target the needs of only a subset of the regional species pool. SCP methods would help to ensure that GI provides a balanced solution for the requirements of many biodiversity features (e.g., species, habitat types) and ES simultaneously in a cost-effective manner. Such tools are necessary to make GI into an operational concept for combating biodiversity loss and promoting ES.

Across large parts of the world, wildlife has to coexist with human activity in highly modified and fragmented landscapes. Combining concepts from population viability analysis and spatial reserve design, this study develops efficient quantitative methods for identifying conservation core areas at large, even national or continental scales. The proposed methods emphasize long-term population persistence, are applicable to both fragmented and natural landscape structures, and produce a hierarchical zonation of regional conservation priority. The methods are applied to both observational data for threatened butterflies at the scale of Britain and modelled probability of occurrence surfaces for indicator species in part of Australia. In both cases, priority landscapes important for conservation management are identified.