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HANSEN, A. J.; J. ROTELLA,; M. KRASKA,; D. BROWN,. "DYNAMIC HABITAT AND POPULATION ANALYSIS: AN APPROACH TO RESOLVE THE BIODIVERSITY MANAGER'S DILEMMA.(Statistical Data Included)." Ecological Applications. Ecological Society of America. 1999. HighBeam Research. 10 Jan. 2012 <http://www.highbeam.com>.
HANSEN, A. J.; J. ROTELLA,; M. KRASKA,; D. BROWN,. "DYNAMIC HABITAT AND POPULATION ANALYSIS: AN APPROACH TO RESOLVE THE BIODIVERSITY MANAGER'S DILEMMA.(Statistical Data Included)." Ecological Applications. 1999. HighBeam Research. (January 10, 2012). http://www.highbeam.com/doc/1G1-60949683.html
HANSEN, A. J.; J. ROTELLA,; M. KRASKA,; D. BROWN,. "DYNAMIC HABITAT AND POPULATION ANALYSIS: AN APPROACH TO RESOLVE THE BIODIVERSITY MANAGER'S DILEMMA.(Statistical Data Included)." Ecological Applications. Ecological Society of America. 1999. Retrieved January 10, 2012 from HighBeam Research: http://www.highbeam.com/doc/1G1-60949683.html
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Abstract. Biodiversity managers face a dilemma of choosing between "coarse-filter" approaches that deal with the habitats of several species and "fine-filter" approaches that address population viability of one or a few species. We present an approach for local spatial scales that integrates habitat-based and population-based methods to focus research and management on the species in a community that are most at risk of extinction and on the places in the landscape most important to these species. The steps in Dynamic Habitat and Population (DHP) Analysis are:
1) determine which species in the planning area most merit field study based on existing data;
2) use local field data to select species that most merit demographic study;
3) use demographic data to model population viability of the species deemed most at risk;
4) design and evaluate alternative management strategies for key species and landscape settings.
We review each step and provide an example for land birds in a portion of the Greater Yellowstone Ecosystem. Among the 143 species of land birds likely in the study area, we selected 13 species most at risk of extinction. These were mostly neotropical migrant passerines that specialized on low-elevation deciduous habitats that may serve as population source areas. We present a management plan for the multiple ownerships of the study area that seeks to maintain/restore population source habitats for key species.
DHP Analysis provides a framework for biodiversity management for those regions identified as high priority for conservation by continental-scale assessment programs such as Gap Analysis. Our approach is designed to minimize local extinctions, which should reduce the risk of range-wide extinctions.
Key words: biodiversity; conservation; Gap Analysis; Greater Yellowstone Ecosystem; management; population viability; risk assessment; species prioritization.
INTRODUCTION
It is widely appreciated that managers of biodiversity must prioritize conservation efforts so that the ecosystems and species most at risk of extinction are given appropriate attention (Scott et al. 1987, Milsap et al. 1990, Avery 1994, Allendorf et al. 1997). Several approaches have been applied at various spatial scales to rank the vulnerability of ecosystems and species, and to more intensively manage those most at risk (Table 1). Generally applied at the physiographic province to continental scales, Gap Analysis (Scott et al. 1993, Scott et al. 1996) and Critical Ecosystems Analysis (Noss and Cooperrider 1994) are used to identify the ecosystems supporting numerous and/or unique species that are poorly protected. Similarly, Species Prioritization schemes (Masters 1991, Milsap 1995) identify the species within a province to continental area that are likely at high risk of extinction. For watershed scales, Ecological Process Management (e.g., Boyce 1991, Cissel et al. 1994) seeks to maintain key ecological processes like disturbance and succession in order to maintain species adapted to these processes. Dynamic Habitat Modeling (Hansen et al. 1993, White et al. 1997) assumes that species abundances are related to habitat suitability and simulates change in habitats across watersheds under alternative management scenarios. Finally, Population Viability Analysis conducts demographic analyses of single species to assess likelihood of extinction.
TABLE 1. Current approaches for prioritizing and managing ecosystems and species at various spatial scales and the new method presented in this paper (the new method is in bold type).
Name Scale Gap Analysis/ Province to Critical continental Ecosystems Species Province to Prioritization continental Ecological Process Watershed to Management province Dynamic Habitat Watershed to Modeling province Dynamic Habitat Watershed to and Population province Analysis Population Viability Watershed to Analysis range Name Concept Gap Analysis/ Identify ecosystems that Critical contain species and/or Ecosystems processes that are poorly protected so that local management can be ap- plied. Species Identify species most at Prioritization risk so that management can be directed at them. Ecological Process Maintain key ecological Management processes and landscape structures to maintain species adapted to these conditions, Dynamic Habitat Quantify/project change in Modeling suitable habitats for each species under varying management scenarios. Dynamic Habitat Use a hierarchical set of and Population filters to identify and Analysis manage the species and places most at risk. Population Viability Analyze population de- Analysis mography to assess risk of extinction, Name Method Gap Analysis/ Rank ecosystems based on Critical native species, threats to Ecosystems ecosystem, and other factors. Species Use existing life history Prioritization and other data to rank species viability. Ecological Process Analyze interactions Management among ecological pro- cesses and structures and manage to maintain them. Dynamic Habitat Hansen et al. (1993), Modeling (1995), White et al. (1997) Dynamic Habitat This paper and Population Analysis Population Viability Use complex demographic Analysis models to assess species viability under varying management strategies. Name Examples Gap Analysis/ Nature Conservancy Critical (1982), Scott et al. Ecosystems (1993), Noss and Cooperrider (1994) Species Millsap et al. (1991), Prioritization Hunter et al. (1993) Ecological Process Boyce (1991), Cissel Management et al. (1994) Dynamic Habitat Modeling Dynamic Habitat and Population Analysis Population Viability Shaffer (1981), Mur- Analysis phy and Noon (1992)
Most of these approaches are so-called "coarse-filter" (Nature Conservancy 1982, Hunter 1990, Noss and Cooperrider 1994) in that they manage key ecosystems or habitats in hopes of maintaining the species within them. Population Viability Analysis (PVA), in contrast, is termed a "fine-filter" approach because it focuses on demography of individual species.
Choosing between coarse- and fine-filter approaches might be termed the "biodiversity manager's dilemma." Most managers seek to maintain viable populations of native species in order to avoid local and range-wide extinctions. In the United States, the National Forest Management Act of 1976 directs the maintenance of viable populations of native vertebrates that are well distributed across their ranges. Managers could best comply with this law using fine-filter approaches. However, the demographic data needed are often difficult and expensive to obtain. Estimating population vital rates and densities usually requires extensive field study. Moreover, these rates vary spatially in many ecosystems, necessitating knowledge about dispersal and spatially explicit field study and population modeling (Dunning et al. 1995). Such intensive single-species studies can cost hundreds of thousands to millions of dollars per year (Mann and Plummer 1992). Because of these costs, Population Viability Analysis is often done only for economically valuable species (e.g., game species) or endangered species, while the demographies of the myriad of other species in a planning area are ignored.
The alternative coarse-filter approach assumes that the population status of species is correlated with habitat availability. In this case, the area and spatial patterning of ecosystems or habitat types are quantified and rule-based or statistical functions are applied that predict the presence or abundance of a species as a function of the habitat. While the coarse-filter approach is attractive in being less costly and allowing many species in a community to be considered, it has serious limitations (Hansen et al. 1993, Scott et al. 1993, Conroy and Noon 1996). The accuracy of the habitat models in predicting species presence or abundance is seldom quantified, and is likely to be variable and often low if local field data are not used. Also, the abundance of a species in a habitat may not be indicative of rates of survival and reproduction in that habitat (van Horne 1983). For example, a species may be abundant in a habitat where reproduction does not replace mortality if there is immigration into the habitat from other population source habitats (Pulliam 1988, Pulliam and Danielson 1991). Such source/sink dynamics are probably common in landscapes where resources and conditions ere relatively heterogeneous in space (Pulliam 1996; Hansen and Rotella 1999). Under the coarsefilter approach, then, species could undergo population declines or even extinction without the knowledge of biodiversity managers.
The biodiversity manager's dilemma is especially expressed at local to regional spatial scales. Detailed demographic studies over entire species ranges are not feasible for more than a few species; hence the coarsefilter approaches are logical at continental scales. Gap Analysis is being applied across the United States, for example, to identify regions rich in native species that are currently poorly protected. What remains unresolved is what combination of coarse- and fine-filter conservation approaches should be applied at local scales to best complement Gap Analysis and other continental-scale coarse-filter approaches. In practice, many managers do not face a dilemma in choosing between biodiversity strategies because funding limits them to coarse-filter approaches. However, an increasing number of studies are finding that spatially mediated population dynamics are prevalent in nature and strongly influence population viability (Pulliam 1996). It is likely that the pressure to integrate fine-filter approaches in biodiversity management will increase in the future, heightening the dilemma.
We propose an approach to help resolve the biodiversity manager's dilemma at local spatial scales (small watershed to regional). Our Dynamic Habitat and Population (DHP) Analysis integrates aspects of Species Prioritization, Dynamic Habitat Modeling, and PVA into a cost-effective management framework. It is designed to bridge coarse-filter studies of habitats with species-specific studies of population demography and viability at local spatial scales. In this paper, we describe the steps in DHP Analysis and illustrate each in an example from the Greater Yellowstone Ecosystem.
GENERAL APPROACH
The basic assumption of the approach is that a subset of the species and places in a locality have the greatest viability risk. A filtering approach is used to focus increasingly detailed study and management strategies on these places and species. As these screens are increasingly expensive to conduct, the result is that available resources are directed toward the species and places that are of the greatest concern. Management strategies to maintain these key species and places in the landscape can then be crafted and implemented.
Once a planning area is defined based on Gap Analysis or other factors, the steps of DHP Analysis (Fig. 1) are applied as follows.
1) Determine which of the species in the planning area most merit field study by ranking each species' viability risk based on range-wide population status, habitat use, and threats to habitat.
2) Screen these selected species based on field study and analyses of local habitat and population factors and range-wide vulnerability scores to determine which most merit field study of local demography.
3) Obtain and use local data on reproduction, survival, and/or dispersal in key places in the landscape to parameterize population models and assess population viability of the subset of species deemed most at risk.
4) Design and evaluate alternative management strategies for the species identified as most at risk and the landscape settings most important to these species.
[Figure 1 ILLUSTRATION OMITTED]
This approach is designed to be hierarchical, so that the latter steps do not necessarily need to be completed to initiate management designs. Financial resources and time may dictate how many of Steps 1-3 are completed before moving to management design in Step 4.
STEP 1: SCREENING BASED ON RANGE-WIDE VARIABLES
A local population has enhanced conservation importance if the species has high threat of extinction across its range. Hence, our first screening involves range-wide measures of population status, habitat use, and threats to habitats. This step is consistent with the several species prioritization studies that have been published (Masters 1991, Millsap et al. 1991, Hansen et al. 1993, Hunter et al. 1993, Avery et al. 1994, Mace 1994, Mace and Collar 1994, Reed 1995, Carroll et al. 1996, Hedenas 1996, Lunney et al. 1996, Freitag et al. 1997). Though not yet recognized in conservation biology texts, these prioritization schemes have been widely used to identify the species that merit more intense conservation and management.
The conceptual foundation of Species Prioritization recognizes that not all species are equally prone to extinction. Species that have suffered local extinction often share certain demographic and life history traits (Whitcom et al. 1981, Pimm et al. 1988, Laurance 1991). Species with small population sizes are often at risk due to vagaries in birth and death rates, environmental fluctuation, and random genetic processes (Shaffer 1981). Even moderate to large populations, however, may be prone to extinction if they are sensitive to habitat change by being narrowly …
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