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This project will develop 2 complementary approaches to indexing 'connectivity', to evaluate and improve the effectiveness of plans for maintaining landscape-level biodiversity. It is directly relevant to priority topic 1.3 'Coarse filter approached to maintaining biodiversity at the landscape scale' and 3.1 'Indicators and monitoring systems'. Indicators commonly used for coarse filter monitoring of landscapes include ecosystem representation, seral stages, edge/interior measures, patch sizes, roads and connectivity/fragmentation. Connectivity is one not easily measured, but it is frequently seen as critical by government regulators, environmental groups, landscape ecologists and licensees who interact with these groups. To date, connectivity has typically been addressed 'intuitively', by looking at landscape maps and perhaps drawing 'corridors'. Problems with this approach include: it only recognizes one (large) scale, it assumes only one type of habitat provides connectivity (typically old forest), it does not recognize the dynamic nature of managed landscapes, and it lacks a clear basis in ecological science. It is unclear that this approach captures 'connectivity' for any real organism, let alone the range of organisms implied by 'biodiversity', and it would be difficult to justify rigorously. On the other hand, general landscape indices lack relevance to actual organisms, and provide little guidance about effectiveness of landscape plans. With initial funding from Weyerhaeuser?s adaptive management program, we started to develop an improved approach to indexing connectivity, using the concept of 'idealized species' ? representing a range of habitat requirements for breeding and dispersal (e.g., from old-growth specialists to broader generalists, different responses to stand-level retention, etc.), and a range of home range and dispersal scales. This concept addresses multiple scales, differing habitat use and response to stand-level management, without specific autecological details of individual species. We then independently developed 2 approaches to indexing connectivity, with different ecological bases: 1. Individual dispersal costs, improving on Richards et al. (2002 Cons. Biol. 16:1409-1421), 2. Potential genetic connectedness of sub-populations. The two approaches are complementary, representing different ecological aspects of connectivity. To date, we have developed working programs for both approaches. Output includes quantitative indices of landscape connectivity for different idealized species, and maps indicating aspects of connectivity and 'corridors' in the landscape. We feel that the initial work has great potential to provide meaningful evaluation of connectivity under different land use plans, and to provide spatially explicit guidance for improving connectivity. However, due to Weyerhaeuser?s funding and staff cuts (including the 2 Weyerhaeuser scientists working on this project), the project remains incomplete. Our funding proposal to FSP is to allow us to complete this work. Specifically: 1. To improve the connectivity algorithms based on our initial experience. 2. To effectively summarize the output into concise, meaningful indices for operational users. 3. To demonstrate the approaches on a real landscape unit simulated over time under different scenarios (e.g., variable retention plan, alternative non-VR plan, natural disturbances). [Landscape projections themselves will be done as part of a currently-funded FSP project through UBC]. 4. To report on the approaches to indexing connectivity for government, industry and environmental organizations involved in land use planning. We would also produce a scientific manuscript on this work, probably for the journal Ecology and Society (formerly Conservation Ecology). [5. To develop the two algorithms into user-friendly tools for use by land planners and groups monitoring landscape-level effectiveness in the province. We budget this objective sep ...
Huggard, David J.. 2007. Ecologically-based connectivity indices for landscape monitoring. Forest Investment Account (FIA) - Forest Science Program. Forest Investment Account Report
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