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10 - Simulating future uncertainty to guide the selection of survey designs for long-term monitoring
- Edited by Robert A. Gitzen , University of Missouri, Columbia, Joshua J. Millspaugh, University of Missouri, Columbia, Andrew B. Cooper, Simon Fraser University, British Columbia, Daniel S. Licht
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- Book:
- Design and Analysis of Long-term Ecological Monitoring Studies
- Published online:
- 05 July 2012
- Print publication:
- 07 June 2012, pp 228-250
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Summary
Introduction
A goal of environmental monitoring is to provide sound information on the status and trends of natural resources (Messer et al. 1991, Theobald et al. 2007, Fancy et al. 2009). When monitoring observations are acquired by measuring a subset of the population of interest, probability sampling as part of a well-constructed survey design provides the most reliable and legally defensible approach to achieve this goal (Cochran 1977, Olsen et al. 1999, Schreuder et al. 2004; see Chapters 2, 5, 6, 7). Previous works have described the fundamentals of sample surveys (e.g. Hansen et al. 1953, Kish 1965). Interest in survey designs and monitoring over the past 15 years has led to extensive evaluations and new developments of sample selection methods (Stevens and Olsen 2004), of strategies for allocating sample units in space and time (Urquhart et al. 1993, Overton and Stehman 1996, Urquhart and Kincaid 1999), and of estimation (Lesser and Overton 1994, Overton and Stehman 1995) and variance properties (Larsen et al. 1995, Stevens and Olsen 2003) of survey designs. Carefully planned, “scientific” (Chapter 5) survey designs have become a standard in contemporary monitoring of natural resources.
Based on our experience with the long-term monitoring program of the US National Park Service (NPS; Fancy et al. 2009; Chapters 16, 22), operational survey designs tend to be selected using the following procedures. For a monitoring indicator (i.e. variable or response), a minimum detectable trend requirement is specified, based on the minimum level of change that would result in meaningful change (e.g. degradation). A probability of detecting this trend (statistical power) and an acceptable level of uncertainty (Type I error; see Chapter 2) within a specified time frame (e.g. 10 years) are specified to ensure timely detection. Explicit statements of the minimum detectable trend, the time frame for detecting the minimum trend, power, and acceptable probability of Type I error (α) collectively form the quantitative sampling objective.
7 - Assessing the ecological consequences of forest policies in a multi-ownership province in Oregon
- Edited by Jianguo Liu, Michigan State University, William W. Taylor, Michigan State University
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- Book:
- Integrating Landscape Ecology into Natural Resource Management
- Published online:
- 14 January 2010
- Print publication:
- 01 August 2002, pp 179-207
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Summary
Introduction
Advances in landscape ecology, ecosystem management, geographic information systems, and remote sensing have led us from the stand, to the landscape, and to broader scales in natural resources planning and management. As science and management have expanded to these scales, they frequently encompass multi-ownership landscapes. The management and scientific challenges posed by multi-ownership landscapes are especially complex. Species and ecosystems do not recognize legal boundaries between ownerships (Forman, 1995; Landres et al., 1998), and the landscape dynamics of individual ownerships is controlled by a complex of economic, social, political, and biophysical forces. The aggregate ecological conditions of landscapes are controlled by the spatial pattern and dynamics of individual owners and ecological interactions among those ownerships. Solutions to problems of conservation policy and practices for multi-ownership landscapes do not lie in isolated owner-by-owner planning and management. Broader scale approaches are needed. Work in multi-ownership landscapes also reveals the need for increased integration among ecological and social sciences. In most contemporary landscapes, the dominant disturbance regimes are directly or indirectly controlled by human activities. In this chapter we will present a case study to demonstrate the importance of taking a multi-ownership view of landscapes and describe an approach we are developing to assess the effects of different forest management policies on ecological components of a province (i.e., subregion) in coastal Oregon.
Overview of multi-ownership landscape assessments and management
Interest in conservation planning, policy, and management in multi-ownership landscapes is increasing rapidly (Kreutzwiser and Wright, 1990; Davis and Liu, 1991; Keiter and Boyce, 1991; O'Connell and Noss, 1992; Schonewald-Cox et al., 1992; Turner et al., 1996; Wear et al., 1996; Maltamo et al., 1997; Landres et al., 1998).