Identifying Critical Areas

In the past, national parks were often created in places like mountains and deserts with spectacular scenery and little apparent economic value. Other protected areas were designed to manage various game species rather than the habitats and ecosystem processes upon which these and other species ultimately depend. As a result, existing protected areas do not represent all habitats equally, and many habitat types have been nearly or completely lost to agricultural and urban uses. For example, in the United States, only 6 percent of land is protected, and most of these areas are found at higher elevations and on less productive soil (Scott et al. 2001). This trend is typical in many parts of the world, where the ecosystems most suitable for agriculture, such as dry trop ical forests, prairies, and grasslands, are under-represented in protected areas. According to a survey of protected habitats in tropical areas, where a total of 7.7 percent of the land was protected, lowland and montane moist and wet forests were overrepresented, and dry forests were underrepresented (Green et al. 1997). Freshwater habitats are also especially prone to destruction and degradation through channelization, damming, dredging, draining, and the introduction of invasive species. Preservation of marine systems has typically lagged behind efforts in terrestrial areas. Only a very small percentage (less than 1 percent) of marine habitats are protected.

Protected areas, generically defined by the World Conservation Union (IUCN) as "an area of land and/or sea especially dedicated to the protection and maintenance of biological diversity, and of natural and associated cultural resources, and managed through legal or other effective means," (World Conservation Union, 1994) include a wide range of protection levels listed under many different names (such as national park, nature reserve, refuge, or sanctuary). To provide greater international consistency and comparability, IUCN created six broad categories to reflect the level of protection and management of protected areas: levels I—III provide strict protection (national parks, nature reserves, wildlife refuges); level IV involves active management to maintain a species; level V focuses on landscape or seascape conservation; and level VI focuses on sustainable use of natural resources (Green and Paine 1997).

Because existing protected areas fail to include many species, conservation efforts in the 1970s and 1980s focused heavily on greater habitat protection for threatened populations and species. These projects generally aimed to identify and protect the minimum suite of habitat areas that a population or species needed to avoid extinction, a goal shared by current Habitat Conservation Plans under the U.S. Endangered Species Act. Such conservation efforts have tended to focus on larger, more charismatic vertebrate species, such as spotted owls, gorillas, tigers, and giant pandas; many conservationists argue that by protecting the habitats of these so-called flagship or umbrella species, the habitats for many other species are protected as well. Since the 1990s, however, greater emphasis has been placed on the direct analysis and conservation of all biodiversity. These efforts have been conducted in a large number of ways, and there is no consensus on the best ways to identify and conserve biological diversity.

Several approaches to identifying critical areas for protection rely heavily on information about the distributions of relatively well-known taxonomic groups, such as flowering plants, butterflies, mammals, and birds (Olson and Dinerstein 1998; Myers et al. 2000). Hotspot analyses use computer algorithms to search for places, considered biodiversity hotspots, that contain the greatest number of species (species richness) across as many taxo-nomic groups as possible (Myers et al. 2000). For these studies, search algorithms can be configured in various ways, either counting total species richness, for example, or the richness of endemic species found only in limited areas.

Although such analyses have been conducted in terrestrial areas for many years, scientists recently conducted one of the first marine hot spot analyses, using centers of endemism for four groups of species (corals, snails, lobsters, and fishes) to select conservation priorities for coral reefs around the world (Roberts et al. 2002). Although supporters of hotspot approaches argue that these take maximal advantage of known biodiversity data to prioritize areas for conservation, critics frequently question whether the result ing priorities may be biased by differences in the availability and quality of data across groups. Hotspot analyses also seem to be inherently scale dependent. For example, different groups of organisms are likely to have overlapping patterns of hotspot diversity at some scales but not at other scales. Because comparable data across groups tend to be available only at relatively large scales, hotspot studies are generally constrained to much lower spatial resolutions than are useful for most conservation planning and management. As a result of this limitation, other approaches are necessary for identifying critical areas for species at smaller spatial scales. In addition, many conservationists believe that hotspot types of prioritization need to be tempered by triage considerations.

Rather than simply targeting the places with the highest levels of species richness, it may be more effective to preferentially target places: (1) with significant levels of biodiversity; (2) that are under increasing threat of losses; and (3) that possess reasonable opportunities and expectations for effective conservation. Although various hotspot, triage, and other approaches to prioritizing critical areas conceptually overlap and often complement each other to a large degree, divergent perspectives remain and seem to revolve around differences in the spatial and temporal scales of interest. Although some methods may be better suited for identifying longer-term (50-year) priorities on a continental scale, others are better suited for distinguishing options for effective local conservation (for example, small countries, states, and localities) over shorter time frames (3-20 years).

Another approach for integrating species distributions, known as Gap Analysis Program (or GAP), uses Geographical Information Systems (GIS), maps of protected areas, and computer algorithms to search for areas that contain species that are currently unprotected (Scott and Csuti 1997). Such analyses can be strictly limited to known occurrences of species, or it can be extended to expected species distributions through the use of additional data (such as land cover and climatic maps) and statistical models of species occurrences. Once the gaps in the protected status of species have been identified, additional computer algorithms can determine the most cost-effective combinations of sites that would need to be protected to adequately conserve these species. Various modifications are also possible, such as special weightings for the presence of endemic species. Because endemic species are unique to a region and may have special adaptations to their local environments, they are usually of special concern. Through weightings for endemicity, certain areas can be given special consideration for protection even if they are otherwise of low priority because of low species richness or because other resident species are already protected elsewhere.

Although GAP-type approaches were initially designed to maximize the protection of species, the basic protocol has also been directed to identify gaps in habitat protection. With the appropriate habitat maps or other data on habitat distributions, one can identify habitat types that are not adequately included within protected areas. Regardless of whether one is concerned with species or habitats, however, representation and redundancy are important criteria in the design of protected area systems. Representative systems contain either all species or all habitats in relative proportion to their abundance in nature. Redundant systems are ones in which all species are minimally represented by subpopulations in multiple areas, and all habitats are adequately replicated and dispersed, so that catastrophes are unlikely to cause com plete losses of species or habitats. In practice, representation and redundancy need to be balanced so that all parts of biodiversity are present, but rare species and habitats are preferentially protected so that they are less easily lost via human accidents or natural disturbances. Although GAP-type and related reserve design approaches employ many simplifying assumptions that may not reflect the complexities of real environments, they provide systematic tools for identifying critical habitat areas for protection.

In addition to representation and redundancy, conservationists increasingly consider other landscape and ecosystem patterns and dynamics in selecting critical areas for habitat protection (Franklin 1993). Landscapes and seascapes, whether human-dominated or relatively natural, are made up of mosaics of habitat types, with populations in these environments frequently depending on some combination of these habitats in close proximity. For example, although seagrass meadows are frequently considered to be nurseries for many coral reef fishes and mangrove forests provide nurseries for invertebrates, all seagrass and mangrove areas are not of equal importance for any given species. Rather, those nursery habitats that are relatively healthy and close to other habitats used by that species are likely to be most critical for maintaining local populations. Many wetland species, such as turtles and amphibians, also require adjacent upland areas during various stages of their life cycles. Unfortunately, although much U.S. legislation regulates development of wetlands, no legislation adequately protects the adjacent upland habitats and these areas are frequently lost (Burke and Gibbons 1995). Spatial configurations of multiple habitats, in addition to full representation and redundancy, are important factors in the planning of reserve systems and other protective measures.

Most landscapes and seascapes are shaped by continual change brought about by periodic disturbance of various intensities (fires, landslides, storms, pollution, and outbreaks of disease or predators) and ecological succession. Disturbance and succession together regenerate the mix of habitat types upon which species and ecosystems depend. Given the dynamic nature of landscapes, it is often important to protect various areas not just for existing habitats, but also for the new habitats that will be created in these areas through ongoing ecological processes (Pickett et al., 1997). This is equally important for ephemeral, early suc-cessional communities, such as those that appear immediately after a fire, and climax communities that take a long time to develop. The long-term maintenance of biodiversity in protected areas is likely to depend on the ability to design systems that allow for (or mimic through active management) ecological dynamics that structure the diverse habitats in nature.

Finally, for many highly mobile species, the combination of existing and future protected areas will almost certainly not be large enough to sustain viable populations. Consequently, landscape- and seascape-level analyses need to extend beyond protected areas to identify ways in which surrounding habitats can be managed to maintain these mobile species. Such research includes reevaluation of how cities and suburbs can be planned in harmony with natural areas, how agricultural production can become more environmentally friendly and support greater biodiversity, how waterways can be better managed to sustain a broader range of aquatic habitats, and how destructive types and intensities of fishing can be reduced and ultimately eliminated. These environmental problems are socioeconomically and ecologically complex, but biodiversity conservation depends on finding ways that human activities can coexist with the mix of habitats upon which biodiversity depends.

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