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Criterion 1 Indicator 9
  Population Levels of Representative Species From Diverse Habitats, Monitored Across Their Range
A very basic concept is that the number of individuals in a population is important in determining that population’s long-term viability, or in other words, its ability to avoid extinction. Some populations fluctuate up and down on a regular basis. However, a sustained decline in population size indicates a greater probability of extinction. Once a population drops below a critical size, it becomes increasingly vulnerable to extinction through the erosion of genetic variability and demographic processes. Loss of genetic variability results in high mortality and low reproductive rates, increased susceptibility to disease, and a reduced ability to adapt to changing environmental conditions. Small populations encounter demographic problems such as unequal sex ratios or the inability to find a mate, problems that can quickly turn into a downward spiral to extinction.
Biologists define the minimum viable population as the minimum size at which a wildlife population has a reasonable chance of avoiding these problems and continuing to exist over the long term. The size of a minimum viable population is larger for species with low reproduction rates and large population fluctuations than for species with high reproduction rates and/or stable population sizes. The species’ distribution across the landscape and its mobility also affect the size of the minimum viable population. For species of concern, it will be crucial to monitor population size, and to maintain these populations above the level of minimum viability.

Can This Indicator Be Quantified
In Oregon, probably only a single species, the northern spotted owl, has been adequately surveyed so far to estimate its population size or the density of individuals in different regions across a geographic range. However, several programs have collected data that may contribute to the estimation of an index to population density. The specific attributes of each monitoring program or database are listed in Appendix B.
It is important to identify which species are of greatest concern. It may be unwise to use particular species for which population data are available, as indicators of population trends or viability for other species with less data. Such assumptions have often proved to be unwarranted (Mannan, et. al., 1984). Instead, specific goals of the biodiversity assessment should be evaluated. Separate analyses should be done for each species of interest. There are some alternative modeling strategies that may be used to assess the status of species without extensive population information; these strategies are presented in the "Recommended Action for Data Collection" section.
Our knowledge of wildlife abundance and population fitness is likely to remain incomplete into the foreseeable future. Yet, resource managers and policymakers must make decisions now that will affect the long-term viability of Oregon wildlife populations. Ecologists and conservation biologists have developed several analytical methods to support fine-filter strategies for sustaining biodiversity. Two possible approaches are described below.
Population viability analysis — The goal of population viability analysis (PVA) is to project population size or likelihood of extinction for a wildlife species under particular circumstances (Possingham, et. al., 1993). It is important to recognize that PVA represents a process, not a specific model type. The simplest PVA approach uses deterministic, single-population models based wholly on demographic parameters (Beissinger and Westphal, 1998; Boyce, 1992). However, most contemporary analyses include animal birth and deaths that have a certain amount of randomness. These analyses also relate the growth or decline of wildlife populations to habitat quality (Roloff and Haufler, 1997), landscape fragmentation (Lamberson, et. al., 1992), or management effects (Thomas, et. al., 1993). Ecological factors make the analysis more realistic, but also mean that the biologist running the model must gather additional information and deal with greater complexity (Beissinger and Westphal, 1998).
There are no strict prescriptions on what data is necessary for conducting a PVA. The data requirements depend upon the factors built into the PVA model being used. The biologists who do a particular analysis need to decide at the start what questions they are trying to answer, and should understand the demography and ecology of the wildlife population. This information, in turn, will determine what model should be used, and what criteria should be selected (Beissinger and Westphal, 1998; Boyce, 1992). Among the different PVA approaches, deterministic, single-population models are the least data-intensive. Beissinger and Wesphal (1998) have listed the data requirements of the main types of PVA models in their Table 1. Stochastic demographic models better portray the uncertainty surrounding wildlife births and deaths, but require more knowledge of the variation among vital rates and the frequency of catastrophic events. At least double the amount of data is required for analyses in which viability estimates are partly based on differences in the species’ habitat over the landscape or over time (Beissinger and Wesphal, 1998).
Habitat suitability models — Habitat suitability index (HSI) models are specifically developed to make it easier to consider wildlife needs in multidisciplinary natural resource assessments (Schamberger and O’Neil, 1986). The HSI approach is based on deterministic models that link the availability of important habitat components (such as snags or downed trees) and landscape characteristics to measures of carrying capacity for a given species or group of species with similar habitat needs. Scientific wildlife research may be used to develop the models and quantify habitat relationships, but more often the models are synthesized from previous studies and expert opinion. Recently, there have been advances in remote sensing technology and GIS. These advances have made it possible to apply more precise habitat models to regional-scale assessments. In Oregon, two contemporary projects that use this approach are the Coastal Landscape Modeling Analysis Study (CLAMS) and the Umpqua Land Exchange Project (ULEP 1999).
Figure 9-1 is a generalized representation of the information that is needed for a spatially explicit HSI model, or in other words, a model based on actual data about a specific area. The data required varies depending upon the criteria used in a particular model. Early HSI models emphasized within-patch relationships, but contemporary models often include habitat associations at the scale of landscapes or home ranges. Habitat suitability assessments are typically based on vegetation data acquired from forest stand inventories, aerial photographs, or satellite imagery. This data is then combined in a database (for example, GIS) with other required input (e.g., stream or road locations).
Other models can be used to characterize forest conditions if empirical data is unavailable, or to predict conditions in the future. For example, tree mortality can be estimated from a tree growth and yield model (e.g., ORGANON); this estimate could be used, with other information, to forecast the abundance of snags and downed logs in forest stands. HSI models can be validated by using available data from previous wildlife research. One way to validate an HSI model is to develop a database of landscape patches known to be occupied by individuals or breeding pairs of a species, as well as an equal number of randomly selected patches. Each patch is then scored for habitat suitability using the test model. If the model is to prove useful in identifying high quality patches in the landscape, occupied patches should be expected to receive significantly greater HSI scores than random patches.
Figure 9-1. Schematic representation of general information and models used to estimate wildlife habitat suitability index (HSI) scores.

Under "Recommended Actions for Data Collection" below, we have identified several ongoing programs that monitor wildlife populations, that may also have the capability to estimate population size or densities across the range of the species in the future. Currently, most estimates of wildlife population densities are the result of hundreds of independent studies, usually conducted on only a few sites. Few of these past research projects have met the statistical assumptions necessary to use them to make inferences beyond the study’s original site. The Oregon Department of Fish and Wildlife conducts surveys of most game species every year, and reports the abundance of these species in an annual report. This data may potentially be suitable to estimate animal density over time, but survey methodologies are not well documented in the ODFW reports.

Data Source and Availability
Data sources used for this indicator are listed under "Selected References."

Reliability of Data
See the "Reliability of Data" section under Indicator #8.

See the "Scale" section under Indicator #8.

Recommended Action for Data Collection
For data-based estimates of population size or density, the easiest approach is to use information from existing monitoring programs (e.g., BBS, ODFW game surveys, federal T&E species monitoring programs) as possible. Population data is most extensive for the northern spotted owl and a handful of other species-at-risk. These monitoring programs are continuing to collect data and may be able to support statistical estimation procedures for additional species in the future, particularly common forest birds and a broader list of threatened or endangered species.
However, we are not aware of any coherent population inventory or monitoring program for non-game mammals that is similar to the BBS or North American Amphibian Monitoring Project. Population sizes and densities have been estimated for other vertebrates by many independent research projects. It is conceivable that data from different studies might be synthesized into a defensible comparison of population densities across the entire range of a species. However, such analyses are complicated by different sampling methods used by various investigators. Any population studies based on statistical estimation procedures are likely to require intensive data management and analyses, even if sampling data can be used from other studies.
We recommend a twofold approach to evaluating population levels of wildlife species and wildlife biodiversity, based on our review of existing information and our professional experiences. Our recommended approach is to use complementary coarse-filter and fine-filter methods to assess biodiversity (see "Definitions" below). The coarse-filter part of the analysis is an evaluation of the composition and structure of existing vegetation communities in Oregon forests. Habitat loss and degradation are frequently reported to be the greatest threat to wildlife population viability. Researchers need to measure and monitor the structure and landscape pattern of forested ecosystems as the first step to ensuring that there are well-distributed wildlife habitats in a region. Coarse-filter analysis is cost-efficient because it addresses the most crucial population viability issue for many species, and uses existing Oregon Department of Forestry data from forest inventories and satellite imagery.
Several in-depth studies have classified regional vegetation and ecological patterns for Oregon (Clarke, et. al., 1991; Franklin and Dyrness, 1988; Omernik and Gallant, 1986), and could provide a coarse-filter framework. Recent studies have provided insight to natural patterns of variation within or among different vegetation zones or ecological regions (Wallin, et. al., 1996; Agee, 1994; Ripple, 1994; Booth, 1991; Agee, 1990). Researchers could compare the current distribution of forests of different ages or structural classes to historic patterns; this comparison may be a useful index to changes in biodiversity at a community-level. If it is found that a particular forest type, such as old-growth forest, has declined to a historic low, this may signal decreasing population viability for wildlife species that depend on that type of forest. It is relatively straightforward to forecast future biodiversity patterns at this scale, using tree growth models and forest management simulators.
Habitat suitability models or PVA represent fine-filter approaches for monitoring the status of specific wildlife habitats or populations. Data from a coarse-filter analysis can be compared to available matrix models (Raphael and Marcot, 1986; Brown, 1985) as an additional check for habitat representation. Such analyses require little additional computation and can be conducted at relatively low cost. For wildlife species that are of critical concern because of their regulatory status or economic importance, HSI models or PVA can be adapted to existing forest data and assessment goals.

Biodiversity — The variability among living organisms from all sources, including terrestrial, marine, and other aquatic ecosystems and the ecological complexes of which they are a part; this includes diversity within species, between species, and of ecosystems (Heywood and Baste, 1995).
Coarse filter — Strategies for setting biodiversity planning goals based on providing an appropriate mix of ecological communities across a planning landscape, rather than focusing on the needs of specific species (Haufler, 1999).
Effective population size — The average number of individuals in a population which are assumed to contribute genes equally to the succeeding generation (Lincoln, et. al., 1998).
Fine filter — Strategies for setting biodiversity planning goals based on the needs of individual species or guilds of species, thus providing for the needs of those species or guilds (Haufler, 1999).
Habitat — The space used by an organism, together with the other organisms with which it coexists, and the landscape and climatic elements that affect it; the place where an animal or plant normally lives and reproduces (United Nations Environment Programme, 1995).
Minimum viable population — The population size that provides a given probability of persistence of the population for a given amount of time (e.g., a 95 percent expectation of persistence without a loss of fitness for several centuries) (Stork and Samways, 1995).
Species — A group of individuals that have their major characteristics in common and are potentially interfertile (FEMAT 1993).
Sustainability / sustainable use — The use of components of biodiversity in a way and at a rate that does not lead to the long-term decline of biological diversity, thereby maintaining its potential to meet the needs and aspirations of present and future generations (The Convention on Biological Diversity, Nairobi, 1992).

Selected References
Agee, J. K. 1990. The historical role of fire in Pacific Northwest forests. In: Walstad, J. D., and S. R. Radosevich, D. V. Sandberg, editors; Natural and prescribed fire in Pacific Northwest forests, pp. 25-38. Oregon State University Press, Corvallis, OR.
Agee, J. K. 1994. Fire and weather disturbances in terrestrial ecosystems of the eastern Cascades. USDA Forest Service Pacific Northwest Research Station, Portland, OR. General Technical Report PNW-GTR-320. 52 pp.
Bailey, V. 1936. The mammals and life zones of Oregon. USDA Bureau of Biological Survey, Washington D.C. 416 pp.
Beissinger, S. R., and M. I. Westphal. 1998. On the use of demographic models of population viability in endangered species management. Journal of Wildlife Management 62:821-841.
Blaustein, A. R., and J. J. Beatty, D. H. Olson, R. M. Storm. 1995. The biology of amphibians and reptiles in old-growth forests in the Pacific Northwest. USDA Forest Service Pacific Northwest Research Station, Portland, OR. General Technical Report PNW-GTR-337. 98 pp.
Booth, D. E. 1991. Estimating prelogging old-growth in the Pacific Northwest. Journal of Forestry 89:25-29.
Boyce, M. S. 1992. Population viability analysis. Annual Review of Ecology and Systematics 23:481-506.
Brown, E. R., technical editor. 1985. Management of wildlife and fish habitats in forests of western Oregon and Washington, Appendix 19. USDA Forest Service Pacific Northwest Region, Portland, OR. Publication R6-F&WL-192-1985.
Clarke, S. E., and D. White, A. L. Schadel. 1991. Oregon, USA, ecological regions and subregions for water quality management. Environmental Management 15:847-856.
Csuti, B., and A. J. Kimmerling, T. A. O’Neil, M. M. Shaughnessy, E. P. Gaines, and M. M. Huso. 1997. Atlas of Oregon Wildlife. Oregon State University Press, Corvallis, OR. 492 pp.
Droege, S., and A. Cyr, J. Larivee. 1998. Checklists: an under-used tool for the inventory and monitoring of plants and animals. Conservation Biology 12(5):1134-1138.
Dunn, E. H., and J. Larivee, A. Cyr. 1996. Can checklist programs be used to monitor populations of birds recorded during the migration season? Wilson Bulletin 108:540-549.
Franklin, J. F., and C. T. Dyrness. 1988. Natural vegetation of Oregon and Washington. Oregon State University Press, Corvallis, OR. 452 pp.
Freemark, K. C., and Hummon, D. White, D. Hulse. 1996. Modeling risks to biodiversity in past, present, and future landscapes. Technical Report Series Number 268, Canadian Wildlife Service, Headquarters, Environment Canada, Ottawa K1A 0H3.
Gabrielson, I. N., and S. G. Jewett. 1940. Birds of Oregon. Oregon State University, Corvallis, OR. Oregon State Monographs, Studies in Zoology 2. 650 pp.
Geissler, P. H., and J. R. Sauer. 1990. Topics in route-regression analysis. In: Sauer, J. R., and S. Droege, editors; Survey and designs and statistical methods for the estimation of avian population trends. USDI Fish and Wildlife Service, Biological Report 90(1).
Haufler, J. B. 1999. Strategies for conserving terrestrial biological diversity. In: Baydack, R. K., and H. Campa III, J. B. Haufler, editors; Practical approaches to the conservation of biological diversity. Island Press, Washington D.C. 313 pp.
Heywood, V. H., and I. Baste. 1995. Introduction. In: Heywood, V. H., editor; Global biodiversity assessment. United Nations Environment Programme Report. University Press, Cambridge, Great Britain.1140 pp.
Lamberson, R. H., and B. R. Noon, C. Voos, K. S. McKelvey. 1994. Reserve design for territorial species: the effects of patch size and spacing on the viability of the northern spotted owl. Conservation Biology 8(1):185-195.
Lande, R. 1988. Extinction thresholds in demographic models of territorial populations. American Naturalist 130:624-635.
Lincoln, R., and G. Boxshall, P. Clark. 1998. A dictionary of ecological, evolution, and systematics. Cambridge University Press, Cambridge, UK. 361 pp.
Link, W. A., and J. R. Sauer. 1994. Estimating equations estimates of trend. Bird Populations 2:23-32.
Lint, J., and B. Noon, R. Anthony, E. Forsman, M. Raphael, M. Collopy, E. Starkey. 1999. Northern spotted owl effectiveness monitoring plan for the Northwest Forest Plan. USDA Forest Service Pacific Northwest Research Station, Portland, OR. General Technical Report PNW-GTR-440. 43 pp.
Madsen, S., and D. Evans, T. Hamer, P. Henson, S. Miller, S. K. Nelson, D. Roby, M. Stapian. 1999. Marbled murrelet effectiveness monitoring plan for the Northwest Forest Plan. USDA Forest Service Pacific Northwest Research Station, Portland, OR. General Technical Report PNW-GTR-439. 51 pp.
Maj, M., and E. O. Garton. 1994. Appendix B: fisher, lynx, wolverine summary of distribution information. In: Ruggiero, L. F., and K. B. Aubry, S. W. Buskirk, J. L. Lyon, W. J. Zielinski, technical editors; The scientific basis for conserving forest carnivores: American marten, fisher, lynx and wolverine in the Western United States. USDA Forest Service Rocky Mountain Forest and Range Experiment Station, Fort Collins, CO. General Technical Report RM-254. 184 pp.
Mannan, R. W., and M. L. Morrison, E. C. Meslow. 1984. The use of guilds in forest bird management. Wildlife Society Bulletin 12:426-430.
Marcot, B. B., and R. Holthausen. 1987. Analyzing population viability of the spotted owl in the Pacific Northwest. Transactions of the conference on North American wildlife and natural resources 52:333-347.
Marshall D. B. 1992. Sensitive vertebrates of Oregon. Oregon Department of Fish and Wildlife, Portland, OR.
Nussbaum, R. A., and E. D. Brodie Jr., R. M. Storm. 1983. Amphibians and reptiles of the Pacific Northwest. University of Idaho Press, Moscow, ID. 332 pp.
Oregon Department of Forestry. 1999. Oregon’s First Approximation Report. Salem, OR. Unpublished report.
Olterman, J. H. 1972. Rare, endangered, and recently extirpated mammals in Oregon. Thesis for master’s of science degree. Oregon State University, Corvallis, OR. 134 pp.
Omernik, J. M., and A. L. Gallant. 1986. Ecoregions of the Pacific Northwest. US Environmental Protection Agency, Corvallis, OR. EPA/600/3-86/033. 39 pp.
Oregon Natural Heritage Program. 1998. Rare, threatened and endangered species of Oregon. Portland, OR. 92 pp.
Possingham, H. P., and D. B. Lindenmayer, T. W. Norton. 1993. A framework for the improved management of threatened species based on population viability analysis (PVA). Pacific Conservation Biology 1:39-45.
Puchy, C. A., and D. B. Marshall. 1993. Oregon wildlife diversity plan. Oregon Department of Fish and Wildlife, regional nongame wildlife biologists, and nongame task force.
Raphael, M. G., and B. G. Marcot. 1986. Validation of a wildlife-habitat-relationships model: vertebrates in a Douglas-fir sere. In: Verner, J., and M. L. Morrison, C. J. Ralph, editors; proceedings of symposium on Wildlife 2000: modeling habitat relationships of terrestrial vertebrates. Oct. 7-11, 1984, Fallen Leaf Lake, CA. University of Wisconsin Press, Madison, WI. 470 pp.
Ripple, W. J. 1994. Historic spatial patterns of old forests in western Oregon. Journal of Forestry 92:45-49.
Rymon, L. M. 1969. A critical analysis of wildlife conservation. Doctoral dissertation. Oregon State University, Corvallis, OR. 429 pp.
Sauer, J. R., and B. G. Peterjohn, W. A. Link. 1994. Observer differences in the North American Breeding Bird Survey. Auk 111:50-62.
Schamberger, M. L., and L. J. O’Neil. 1984. Concepts and constraints of habitat model testing. In: Verner, J., and M. L. Morrison, C. J. Ralph, editors; Wildlife 2000: modeling habitat relationships of terrestrial vertebrates, pp. 5-10. Oct. 7-11, 1984, Fallen Leaf Lake, CA. University of Wisconsin Press, Madison, WI. 470 pp.
Scott, J. M., and B. Csuti, G. Wright, P. J. Crist, M. D. Jennings. 1999. Regional approaches to managing and conserving biodiversity. In: Baydack, R. K., and H. Campa III, J. B. Haufler, editors; Practical approaches to the conservation of biological diversity. Island Press, Washington D.C. 313 pp.
Starfield, A. M. 1997. A pragmatic approach to modeling for wildlife management. Journal of Wildlife Management 61:261-270.
Summers, S., and C. Miller. 1993. Preliminary draft: Oregon county checklists and maps. OFO Special Publication No. 7. 102 pp.
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Thomas, J. W., and M. G. Raphael, R. G. Anthony, E. D. Forsman, A. G. Gunderson, R. S. Holthausen, B. G. Marcot, G. H. Reeves, J. R. Sedell, D. M. Solis. 1993. Viability assessments and management considerations for species associated with late-successional and old-growth forests of the Pacific Northwest: the report of the scientific analysis team, Appendix 5-A. USDA Forest Service, Washington, D.C.
ULEP. 1999. Independent science peer review of the pilot study methodology: Umpqua land exchange project.
US Geological Survey. 1999a. North American Breeding Bird Survey (NABBS)
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USDA Forest Service, et. al. 1993. Forest Ecosystem Management: An Ecological, Economic, and Social Assessment. July 1993. Also known as the FEMAT Report. USDA Forest Service, Pacific Northwest Region, Portland, OR.
USDI Fish and Wildlife Service. 1981. Standards for the development of habitat suitability index models. 103 ESM. Washington D.C. Release No. 1-81.
Van Horne, B, and J. A. Wiens. 1991. Forest bird habitat suitability models and the development of general habitat models. USDI Fish and Wildlife Service, Washington D.C. Fish and Wildlife Research 8. 31 pp.
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