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5 Local and Regional Indicators INTRODUCTION Indicators are needed to inform us about ecological status and trends at all spatial and temporal scales, and at a variety of levels of specific ity, ranging from the status of local populations to the functioning of large ecosystems. Because space and time are both continuous variables, scales of applicability of indicators blend into one another. Indeed, many indicators are useful at several scales. For example, the indicators we recommend in this chapter for forest condition can be aggregated usefully at regional, national, and continental scales. In addition, most policy and management decisions are made at scales defined by laws and regulations established by political entities, such as local municipalities, counties, states, and the federal government. Although the committee focused its attention on the national-level ecological indi- cators recommended in Chapter 4, the methods used to select and formu- late those indicators are equally applicable to indicators designed for use at smaller spatial scales. Further, many national-level indicators can be reported at various levels of disaggregation to serve as regional ecological indicators. In this chapter, we examine a number of local and regional indicators that we judge to be especially important, and show how they can be computed and interpreted. 116

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LOCAL AND REGIONAL INDICATORS PRODUCTIVITY INDICATORS 117 In addition to a national-level indicator of ecosystem productivity, it is also useful to have indicators specifically designed to capture the performance of particular ecosystem types. In this discussion, we give examples of indicators for forested ecosystems. Similar indicators can and should be developed for other vegetation types, such as grasslands, savannas, deserts, and wetlands. FORESTS AS AN EXAMPLE For regional forest indicators, we recommend indicators of produc- tivity and species diversity, structural diversity, and sustainability. These attributes support the continued provision of the following goods and services from forests: wood and wood products, opportunities for recrea- tion, tourism, and aesthetic enjoyment, maintenance of wildlife resources, control of erosion and nutrient losses to surface waters, and mitigation of . . . greennouse-gas emissions. The most valuable indicators for forests are those that can provide early warning of adverse trends in productivity, species diversity, and structural diversity. Productivity integrates the flow and storage of car- bon with flows of nutrients, water, and light. It provides the sustained yield of wood products. In addition, with the increased concentrations of carbon dioxide in the atmosphere, management of forests for carbon stor- age has assumed great importance (Cooper 1983, Harmon et al. 1990~. Species diversity is also an important indicator of the condition of forests, if for no other reason than that most species on the Endangered Species List inhabit forests (Doyle 1998~. Structural diversity of forests includes such features as crown condition, foliage-height profiles, and amounts and status of coarse woody debris; these attributes are all important for animal habitat (MacArthur et al. 1962; Franklin et al. 1981, 1989; Franklin and Forman 1987; Spies et al. 1988~. The three features of forests that indicators address also provide opportunities for recreation and tourism and contribute towards maintaining the aesthetic quality of the nation's forests. The development of a program for monitoring the status and trends of the nation's forested ecosystems is a continuing research effort that has sound practical underpinnings. Continuous inventory programs of the U.S. Forest Service form the basis of monitoring various aspects of forest structure and productivity. These inventory programs are positioned to take advantage of the existing theoretical base provided by individual- tree simulation models (Shugart 1984~. We first review the current forest inventory programs that provide

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118 ECOLOGICAL INDICATORS FOR THE NATION the empirical basis for forest-status monitoring. We then discuss those current forest ecosystem models that may provide a theoretical basis for evaluating forest functioning, as well as for forecasting future status and trends in forest productivity and diversity. Based on this review, we then recommend specific indicators of the status of forests, focusing on how these indicators relate to current inventory programs and ecosystem models. Current Forest Inventory Programs The U.S. Forest Service has periodically inventoried the status of the nation's forests through its Forest Health Monitoring Program (FHMP) and the older Forest Inventory and Analysis (FIA) Program. A nation- wide network of plots for the FHMP has been partially implemented (Anonymous 1996~. In this program, forests are inventoried in plots spaced every 27 km on a nationwide grid network. The plot network currently exists in 15 states, mainly in the Northeast and Southeast, and in scattered areas in the rest of the country. At each grid point, various subplots are sampled for a variety of attributes. The size of each subplot is determined by the ecological scale of each attribute: the subplots range in area from 2 m2 to 1 ha. These plots are sampled every four years for traditional timber-yield data on tree-diameter distributions, tree species, and site index. Canopy condition, leaf-area index, lichen communities, scenic beauty, lichen chemistry, foliar chemistry, dendrochemistry, dendro- chronology, branch evaluation, browse supply, and root condition are also assessed (Anonymous 1996~. The FHMP currently covers 70 percent of all forested lands in the coterminous United States. When fully imple- mented in 2002, the FHMP system will provide detailed data with reason- able spatial and temporal coverage to detect regional problems in the nation's forests. The FHMP system provides detailed data on relatively few plots. In contrast, the FIA system provides extensive coverage on fewer attributes, mainly those related to timber volume and forest productivity. In the FIA program, permanent plots located on lands of all ownership types are inventoried every decade, mainly to evaluate standing crop of timber, but also in many cases for assessing understory vegetation, tree seedling regeneration, disease indicators, and browse availability. For example, in Minnesota alone, more than 10,000 plots have been inventoried in this manner since before 1960 in a cooperative program between the U.S. Forest Service and the Minnesota Department of Natural Resources. These data form the basis for policy decisions in Minnesota regarding timber supply from public lands and were the basis for the recently com-

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LOCAL AND REGIONAL INDICATORS 119 pleted Generic Environmental Impact Statement for Expansion of the Pulp and Paper Industry in Minnesota (Jaakko-Poyry Consulting 1992~. The FIA plots provide information on diameter, height, and species of all trees and numbers of seedlings by species. From these data, biomass, tree species diversity, and mortality can be calculated. On some plots, browse supply and condition are also measured to evaluate ungulate habitat. These FIA data can corroborate trends measured in more detail in the FHMP plots, and they provide more detailed coverage of the productivity and diversity of forested lands. To be of even more use, the FIA system requires more complete data archiving and quality control. In particular, the locations of the FIA plots need to be determined accurately using global-positioning systems, rather than the current method of permanent stakes and survey markers, which are sometimes lost. Finally, additional FIA plots need to be established to reflect more accurately the distribution of land in various ownerships. For example, although the FIA plots in Minnesota are distributed across all ownership types, there are few FIA plots on national forest land in other states, particularly in the Pacific Northwest. Current Simulation Models of Forest Ecosystems The raw field data collected by the FHMP and FIA programs can be imported into individual tree-based ecosystem models (Shugart 1984) to project potential trends, given hypotheses about how various stressors (e.g., climate change, acid rain, and harvesting) affect tree physiology and stand population dynamics. The development of these ecosystem models in recent decades provides a theoretical basis for analysis and projection of the data (see examples reviewed in Agren et al. 1991, Mladenoff and Pastor 1993, Pastor and Mladenoff 1993~. These models project the diameter and height growth of individual trees on a plot approximately of 0.1 ha (approximately the same scale as the FIA and FHMP plots), subject to constraints of growth limitations, including light limitations through shading, temperature, water, and nutrients (Shugart 1984, Pastor and Post 1986, Pacala et al. 1996~. The models have been tested against independent data on successional trends, productivity, species diversity, and nitrogen cycling throughout eastern North America (Shugart 1984, Pastor and Post 1986, Pastor et al. 1987, Pacala et al. 1996~. Versions of these models also exist for the Pacific Northwest (Keenan et al. 1995~. An example of using one of these models to determine plot sampling regimes for monitoring status and trends is detailed in Appendix A. The combination of established, long-term monitoring programs and a suite of extensively tested simulation models operating at the same scale as the data provides a sound basis for an integrative program to assess the

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20 ECOLOGICAL INDICATORS FOR THE NATION status and trends of the condition of the nation's forests. We now turn to specific recommendations for indicators that will enhance the usefulness of these models and inventory programs. Recommended Indicators for the Status of the Nation's Forests We recommend that the following forest indicators be given high priority: (1) productivity and tree species diversity, (2) soils, (3) light pen- etration, (4) foliage-height profiles, (5) crown condition, and (6) physical damage to trees. We recommend indicators that can be assessed with a small amount of time spent collecting data on site, that would be ame- nable to calculation of other synthetic indices (such as various diversity indices) later in the laboratory or office, and that could be easily incorpo- rated into existing inventory programs. 1. Productivity and Tree Species Diversity. Productivity and tree species diversity form the basis of the forest food web; this web is sustained by the ability of soils to provide water and nutrients and by the ability of the crown to capture light. The FHMP and FIA programs already collect the data required to assess the status and trends of productivity and tree species diversity. These data include tree diameters at breast height, tree heights, density by species, height classes at which species occur, and canopy cover for each species within each height class. From these data, carbon storage and net primary productivity of trees can be calculated, as well as various diversity indices. 2. Soils. The soil profile should be characterized from a one-time sampling to characterize structure, texture, and rooting depth (Soil Survey Staff 1993~. These physical features determine the ability of the soil to hold water at depths at which it is available to trees. Because these prop- erties are relatively permanent, there is no need to reinventory them, except perhaps after several decades. In contrast to water-holding capac- ity, soil-nutrient availability can change fairly rapidly. Therefore, decadal sampling of the soil is required to determine changes in soil organic mat- ter, total nitrogen, exchangeable cations, and forest floor chemistry (C/N ratio, lignin content, and P. K, Ca, and Mg contents). In addition, nitrogen availability should be assessed by means of resin bags (Binkley 1984~. Doing so would require that the plots be revisited the following year to retrieve the bags, but this technique is able to assess rapid changes in availability of limiting nutrients (Pastor et al. 1998~. Resin bag measure- ments therefore can serve as an early warning system of trends in soil productivity. The indicator of soil status (soil organic matter, recom- mended in Chapter 4) could enhance the soils component of the FHMP and FIA.

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LOCAL AND REGIONAL INDICATORS 121 3. Light Penetration. Any disturbance to the canopy (or recovery of canopies from prior disturbance) necessarily changes light penetration. Light penetration can be measured easily by means of vertical photo- graphs taken with a fish-eye lens, images that can then be analyzed later in a laboratory after they have been scanned into a computer. Further- more, because changes in light penetration are the major driving force behind succession (Pacala et al. 1996), these data would be useful for projecting status and trends by means of the individual-tree models. 4. Foliage-Height Profiles. Foliage-height profiles are relatively easy to measure and provide a relative index of bird species diversity and possi- bly insect diversity. They are therefore an important measure of structural diversity. The vertical structure of the canopy, specifically foliage-height diversity, is strongly correlated with bird species diversity (MacArthur 1959, MacArthur et al.1962, 1966~. Foliage-height diversity varies tempo- rally with canopy development during succession (Aber 1979a) and spa- tially along soil moisture gradients (Aber et al. 1982~. This diversity can be measured rapidly by means of a camera that is mounted vertically and used as a range-finder (Aber 1979b). The vertical distribution of leaf area and therefore potential bird species diversity can then be calculated from these data. Such data are also useful for assessing changes in growth as well as its efficiency and allocation in forest stands (Ford 1982, Beadle et al. 1982, Waring 1982~. New technological developments may make it easier to collect such data. In particular, NASA has developed an instrument, the Vegetation Canopy Lidar (VCL), that can measure foliage-height profiles from above. The functioning prototype has operated successfully from the Space Shuttle and is now mounted on an aircraft. An updated version is being constructed for launch on a small satellite with a projected launch date of late 1999 or early 2000. This instrument will have a horizontal resolution of 20 m and is intended to map canopy heights over the entire surface of the planet once during its two-year lifetime. This goal may not be achieved, but to compare aerial or satellite imagery with stand data, extremely accurate spatial positions must be known for the stands, data that can be obtained only with global positioning systems. 5. Crown Condition. Crown condition, which reflects the state of the canopy that accounts for productivity, is correlated with tree energy status. Trees with a history of poor crowns usually grow more slowly and have diminished energy and carbon reserves. The latter characteristics translate into less carbon being available for defense against insects and pathogens and repair of damage caused by biotic or abiotic agents; en- ergy reserves are critical in surviving periods of stress. Trends in damage and crown condition are usually accurate indicators of trends in produc- tivity and mortality. If severe enough, damage and crown condition may

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22 ECOLOGICAL INDICATORS FOR THE NATION be useful predictors of mortality (e.g., Silver et al. 1991), although more general quantitative relationships have not been developed for those parameters. Useful measurements of crown condition are crown ratios (percent- age of tree height that supports live foliage), crown diameters, density, transparency, and dieback (progressive mortality of branches proceeding from branch tips inward). These data may be combined to give composite measures such as crown volume and crown surface area. Selected mea- surements, such as crown transparency and dieback, may also prove to be useful indicators. Continued evaluation may show how crown volume or crown surface area values can be useful indicators of habitat quality, especially for birds and insects. 6. Physical Damage to Trees. Trees are damaged by insects, pathogens, poor management practices, weather-related stresses, and air pollution, acting alone or in combination. Physical damage to trees after storms, lightning strikes, fire, and logging provides entry points for insect pests and diseases. The extent of damage and trends in damage, recorded by species and age class, can be diagnostic of cause in certain cases, and can provide a relative measure of likelihood that forest diversity, productivity, sustainability, and aesthetic value will be compromised. Combining quantitative measures of damage type and severity with mortality data could eventually provide a quantitative basis for predicting trends in valued forest attributes from damage indicators. Damage categories used by foresters include wounds, evidence of pathogen attack, brooms, broken branches, broken roots, and damaged or discolored foliage, buds, and shoots. Weighting the components based on how likely the damage will effect growth or survival provides an index of the significance of the damage. Implementation The methods of collecting data on these indicators can be learned easily by field foresters in one- or two-day workshops. Most of the data processing would take place later in the laboratory through soil sample analysis or computer analysis. A mere 1 to 2 hours per plot are required to obtain these data (except for the initial, one-time soil profile character- ization, which would require an additional 2 to 3 hours), beyond the time currently spent on collecting the more traditional timber evaluation data. Given the small increase in time spent in the field and the large advantage that would accrue from obtaining these data, such a program should receive high priority for development and implementation.

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LOCAL AND REGIONAL INDICATORS INDICATORS OF SPECIES DIVERSITY 123 In addition to the national indicators of the status of species diversity recommended in Chapter 4, the nation needs indicators to evaluate the diversity status of a local area, such as a national park or an area exploited for human use. For evaluating the diversity status of such areas, we recommend three indicators: Independence of the Area, Species Density, and Deficiency of Natural Diversity. Although we tried to reduce the number of these indicators of diver- sity, and have grounded them in a single well-researched power law, all three are needed because they each inform us about different aspects of diversity. As Angermeier and Karr (1994) noted (about different levels of taxonomic diversity), "no accepted calculus permits integration of counts of elements across levels.... Arguably, no such calculus should be sought." We believe this point applies to diversity measures as well. It follows that the separate aspects of diversity need to be monitored and reported separately. Local assessment of species diversity presents a new problem, because simple counts of species diversity have at least five weaknesses that make them unreliable. Diversity counts are biased by sample size (Fisher et al. 1943~. The larger the sample, the more species in the count. Simple counts are rarely complete, and even when they are, one cannot be sure that they are. Moreover, the species most likely to be missed are the rarest exactly those that most need protection. Diversity counts vary with the extent of the area over which they are measured. Larger areas have more species (Arrhenius 1921), not because they are environmentally better, but simply because they contain more habitats (Williamson 1943~. Diversity counts are biased by the length of the period over which they are measured. More time leads to more species in the raw count (Preston 1960~. Again, the greater number of species results not from improved environmental quality, but simply because longer durations yield greater heterogeneity. A longer period of observation is equivalent to more habitats in space (Rosenzweig 1998), because different species require various seasons and various kinds of years to succeed (Chesson 1994~. Diversity is a dynamic property of ecosystems (Rosenzweig 1995~. Simple counts do not tell us whether diversity is sustainable. The diversity of any area within a continent depends partly on the continental matrix in which that area is embedded (Rosenzweig 1995~. Simple counts ignore that matrix. A species found in a place may persist there only because favorable conditions are accessible elsewhere.

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124 ECOLOGICAL INDICATORS FOR THE NATION Therefore, simple species counts need to be processed and analyzed before being incorporated into indicators. Fortunately, recent advances in the study of community diversity provide us with a number of sophis- ticated practical methods to minimize the sample-size bias (Burnham and Overton 1979, Chao 1987, Chazdon et al. 1998, Colwell and Coddington 1994~. Our recommended local and regional indicators assume the use of these methods, and they also correct for the other deficiencies of simple counts. In Chapter 4 we showed that samples of area contain a number of species, S. that fits a power law, S = cAZ, where A is area, and c and z are coefficients of the equation (Arrhenius 1921, Preston 1962~. A thousand years ago, most of the sample areas monitored today were subsamples of a contiguous whole. They exhibited characteristic z values between 0.10 and 0.20 (Rosenzweig 1995~. Now, however, they are likely to be isolated remnants of the whole, which is a very consequential difference for main- taining diversity. Two types of species contribute to local diversity S (Shmida and Ellner 1984, Pulliam 1988, Pulliam and Danielson 1991~. The first are species whose births exceed their deaths in the area. These are the source species of the sample. Other species, known as sink species, maintain themselves in a sample even though their average birth rate is less than their average death rate, because they frequently immigrate into the area. Isolating an area, as usually happens when a reserve is set aside, cuts it off from the immigration that maintains its sink species (Rosenzweig 1995~. The sink species then eventually vanish from the isolate. This reduces the c value and increases the z value characterizing the area's species diversity. The c value is idiosyncratic to particular taxa and regions, but z for isolates tends to be approximately 0.2 to 0.4, much higher than the z of subsamples (Rosenzweig 1995~. Knowing the relationship of S to area, the quasi-sustainable diversity of an area can be estimated by cA03 (where c is the intercept coefficient of the regional, logarithmic species-area pattern for the taxon being assessed, and A is the size of the area being assessed.) Quasi-sustainable diversity is diversity that should persist for many human generations (although ultimately z rises and S declines, to a degree that is also predictable using the species-area relationship). An Indicator of Independence The quasi-sustainable S suggests an indicator of independence based on the z values most likely to characterize natural ecosystems. To com- pute this indicator, first assess the diversity of an area, Si, and of its whole province, Sw. Let the area of interest be Ai and that of the province Aw. Ii, the measure of independence, is defined as:

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LOCAL AND REGIONAL INDICATORS Ii= [lgSw - logSi] / 0.2[10gAW - logAi] 125 The value 0.2 in the denominator is the threshold z value: z > 0.2 means no sink species. The rest of Ii is the z value of the area. Thus, if Ii > 1, the area probably contains few if any sink species and its diversity is independent of other parts of the province. If, on the other hand, Ii < 1, then some species living in the area rely on other areas for population support. These species need to be identified with more traditional demo- graphic techniques. Their source areas need to be located and preserved as well. An Indicator of Species Density Managers typically wish to optimize the value of their reserves. It might appear that the more species housed in a reserve, the better its condition, but this is not necessarily true. As Chapter 4 showed, the form of the species-area curve means that an adjusted species density reveals more than raw species densities, Si/Ai. Recall from Chapter 4 that the adjusted species density of an area is Di, where Di = Si/AiZ. The greater Di, the more species the preserve maintains relative to the norm. In calculating Di, use the prevailing or average z value for the biologi- cal region. Because experience indicates that z is close to 0.3 in isolates, a value of 0.3 can be used if data are unavailable to estimate z. As in national assessments, high values of Di must be interpreted carefully because they may reflect unsustainable overloading of the area. In par- ticular, if high Di is accompanied by Ii < 1, the high species density is unlikely to persist. To see why this is true, consider an area that is not a reserve, but is used for various residential and commercial purposes. Despite this situ- ation, suppose the area supports many wild species as well. If changing patterns of land use within the area squeeze those species into a more restricted, smaller proportion of the whole, Di will rise and Ii will decline. If it is known that changing uses will remove a certain amount of the area from access by wildlife, the initial value of the higher Di and lower Ii can be calculated in advance. But the increase in Di does not signal environ- mental improvement because it is likely to decline to its former value. Once the effective area diminishes, the only way for the system to return to a sustainable diversity is through reduction in the actual number of species it contains. The increased Di merely signals an impending loss of sit We do not recommend that local diversity managers calculate the M value (see Chapter 4) of their areas instead of these areas' D value. At the local level, the indicator needs dictate whether the area is under- or overdiverse. M deliberately eliminates that distinction.

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26 ECOLOGICAL INDICATORS FOR THE NATION Indicators of Deficiency in Natural Diversity When human uses dominate a landscape, natural assemblages of species disappear, but they are in part replaced by exotic species. In Chapter 4, we recommended a national indicator of native species diver- sity, to indicate the degree to which exotics have replaced native species. A local indicator that quantifies this tendency is also needed. For example, consider the difference between the bird species of Tucson, Arizona, and those of the surrounding natural landscape (Emlen 1974, Table 5.1~. The differences are typical of those seen in commensal assemblages of most or all other taxa, so we describe Emlen's conclusions and use them to design an index of deficiency in diversity, Ui. Tucson sits in an Upper Sonoran Desert basin, surrounded by tracts of natural vegetation and their resident bird species. The city itself is mostly a vast suburb with expanses of vegetation supported by urban irrigation. As a result of extensive watering, the total abundance and biomass of all bird species has risen more than 26-fold, but most of the individuals belong to a few commensal and exotic species, mostly house sparrows, house finches, doves, starlings, and mockingbirds. Since Emlen's study, more curve-billed thrashers, cactus wrens, Gila wood- peckers, Gambel's quail, and pyrrhuloxias have moved into the city. Phainopeplas are more abundant in both the city and the desert. Anna's hummingbirds have virtually displaced the native black-chinned hum- mingbirds, and great-tailed grackles, a new commensal, have become quite common. Some raptors, such as Harris hawks, Cooper's hawks, and great horned owls, are now regularly seen in the city. But the overall difference Emlen observed has not changed in the two decades since he wrote: birds are far more abundant in Tucson than in the surrounding desert, but the city has fewer native species. Common, widespread oppor- tunists and exotics account for most of the urban bird biomass. There are several reasonable and complementary explanations for why anthropogenic habitats often bring about the loss of many native species and the burgeoning of commensals. First, anthropogenic habitats have no evolutionary pedigree; species have not had a chance to adapt to them. Moreover, people continue to change habitats at rates that are likely to prevent species from adapting to them. Nevertheless, a few species have traits that enable them to thrive in highly modified environ- ments. Many sets of species that use similar resources have members that depend for their continued mutual existence on their tolerance of sub- optimal conditions. Tolerant species cannot dominate the "best" habitat patches, and intolerants depend on the best habitats for their survival. When people change a habitat to produce novel conditions, the most

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LOCAL AND REGIONAL INDICATORS 127 TABLE 5.1 Emlen's study of the effect of urbanization on the avian species assemblage. Abundance went up 26-fold but diversity declined from 21 to 15 species. Moreover, many of the local specialties were replaced by exotics (house sparrow, starling) and widely distributed commensals like Inca doves, mockingbirds, cardinals and house finches. Individuals/100 acres Species Desert City Gambel's quail 0.3 White-winged dove 0.5 140 Mourning dove 1.9 30 Inca dove 230 Roadrunner 0.5 Black-chinned hummingbird 6 Gilded flicker 1.9 Gila woodpecker 0.3 14 Ash-throated flycatcher 0.8 2 Verdin 2.5 14 Cactus wren 6.8 2 Curve-billed thrasher 6.9 5 Bendire's thrasher 0.2 Mockingbird 0.3 45 Black-tailed gnatcatcher 1.6 Starling 35 Loggerhead shrike 0.1 Brown-headed cowbird 0.4 1 Hooded oriole 0.6 House sparrow 520 Cardinal 1 7 Pyrrhuloxia 0.6 House finch 0.3 170 Brown towhee 1.2 Black-throated sparrow 16.5 Rufus-winged sparrow 2.5 tolerant species are likely to succeed exuberantly, whereas the intolerant ones become confined to nature reserves. One reason why so many Old World species have moved in and exploded in suburban environments to the detriment of natives may be that they have had more time to adjust to humans. In addition, some species transplanted to new continents simultaneously escape from their predators. For example, an Australian native acacia that is not particu-

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28 ECOLOGICAL INDICATORS FOR THE NATION larly abundant in the western Australian kwongan where it evolved, became a scourge in the similarly poor soils and Mediterranean climate of the similarly diverse fynbos in the Southwestern Cape Province of South Africa. Gypsy moth outbreaks are common in eastern North America, but rare in these insects' native Europe. (Diamond [1997] used similar ideas to understand the broad aspects of the distribution of human civili- zations and the origin of technological advance.) Thus, three factors contribute to the extraordinary abundance of a few species in anthropogenic environments: Exotics may have had more time to adjust to humans. Exotics may have escaped many of their natural predators. Only a subset of native species (the tolerants) are preadapted to "degraded" environments. To evaluate the deficiency of diversity in an area of Tucson, one can- not use the raw value of Di, species density, because it gives the city credit for exotic species that merely follow human settlement, and for tolerant natives that would thrive anywhere. An indicator that depreciates the value of an area according to the proportion of its species that thrive in anthropogenic habitats is needed. There is no shortage of such habitats, and there is not likely to be in the foreseeable future. One way to achieve this would be to recalculate Di, after excluding the contributions of the tolerants and the exotics. However, because not enough is currently known to identify tolerant species, the best that can be done is to exclude the exotics, as was done for NAT IMP (see Chap- ter 4~. Let Gi = Si,n/CA ' where Sin is the number of native species at the site and cAZ gives the number of species expected in a site of area A. (The coefficients c and z are determined for the taxon in areas of the region free of exotics. Therefore, cAZ amounts to an alternative expression for Sn.) Because Gi measures native species density, it makes a better index of local diversity and gives a truer picture of the value of a place in supporting diversity. The complement of Gi is an indicator of true deficiency in diversity, labeled U to signify unnaturalness: Ui = [cAZ _ Si n] /cAZ. Ui measures the proportion of native species expected at a site (of area A) but not found there. Thus, Ui combines the change due to exotic species

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LOCAL AND REGIONAL INDICATORS 129 with that caused by the overall loss of species. Because values of this indicator of the deficiency of natural diversity change relatively slowly with time, decadal monitoring is probably sufficient. As an example, assuming that Emlen's data accurately reflect what to expect of 100 acres of Sonoran desert, one can calculate Ui for 100 acres of Tucson. In 100 acres, cAZ is 21 species. But Sin is 12. Thus, Ui is 9/21 or 0.43. Tucson's 100 acres are 43 percent deficient in natural species diver- sity. Because everything gets scaled nonlinearly by the power law, that deficiency is the same for any amount of area in the city. As another example of an indicator of deficiency of natural diversity, consider the proportion of nonnative fish species in a region, usually a state, because most states have agencies that collect data on the distribu- tion and abundances of fishes, especially game fishes. New techniques are unlikely to change the ease of obtaining the necessary input data. The proportion of nonnative species in the fauna of a state is an imprecise measure because not all exotics are of equal importance. Deciding whether to list an exotic can be tricky. Some anadromous species that invade fresh waters only briefly for spawning are counted as exotics. Other nonnative species, such as guppies in Oregon, survive only in very specific environments, in this case, hot springs. Conversely, introduced trout cannot survive the hot summers in most lowland waters of the eastern United States, but they thrive in cool tailwaters below dams. Some nonnative fishes are temporary survivors that live for only a few weeks or months. Crude state lists do not distinguish such species from wide- spread permanent residents, but the methods described above can adjust an indicator so that very localized or temporary species do not count as much as other exotics. The proportion of nonnative fish species varies greatly among states, from 0.02 (one [anadromous] species out of 56) in Alaska (Morrow 1980) to .47 (36 out of 76) for Washington State (Wydoski and Whitney 1979~. In relatively dry western states with low diversities of native species and high proportions of dammed rivers, the indicator currently has values greater than 0.2, and for many states the values are greater than 0.3. In the wetter eastern states, which also have higher native fish diversities, values are about 0.07 to 0.08. Values in all states are almost certain to increase over time because established exotics are almost impossible to extermi- nate, new introductions continue, and the quality of habitat for native fishes continues to deteriorate in most states. Because most states have agencies that collect data on the distributions and abundances of fishes, especially game fishes, information is typically available by state rather than by region or ecoregion. Often, however, the information is old or not systematic, and so it is not always reliable, especially with respect to diagnosis and distribution of nonnative species.

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130 ECOLOGICAL INDICATORS FOR THE NATION The Index of Biotic Integrity: An Indicator of Species Diversity of Aquatic Ecosystems Additive multimetric indicators have been developed and used to compare the species diversity of aquatic systems with what should be in those systems in the absence of human-caused perturbations (called appro- priate diversity by the NRC [1994~. The most widely used multimetric indicator is the Index of Biotic Integrity (IBI), which has been developed and tested in a variety of aquatic ecosystems (Kerr et al. 1986, Karr and Chu 1999~. The use of IBI requires general agreement about which organ- isms indicate by their abundance or absence, poor or good ecological and water characteristics. The IBI provides a method for quantifying those qualitative assessments. The IBI is primarily a community-level rather than an ecosystem indicator because it is based on taxonomic assem- blages within specific phylogenetic groups and specific biogeographic regions. The original IBI was developed for freshwater fish communities in streams in the Midwest. Recently, similar indicators have been applied to freshwater benthic macroinvertebrate communities in several regions (and even to some terrestrial communities). An IBI is calculated from a set of measures of distribution and relative abundance of selected taxa. Each measure is assigned a numerical value- an integer ranging from 0 to 6 based on the qualitative judgment of the index developers. The final IBI, which is the sum of the individual scores (usually 10 to 12), is unbounded but typically is between 0 and 60. Because individual scores are discontinuous, statistical analysis of the additive scores are generally inappropriate. More detail on the mechanisms for developing multimetric indices was provided by Barbour et al. (1995~. Typically, IBIs are developed for biogeographic systems such as ecoregions where similar communities of organisms are expected. For example, Ohio has developed an extensive set of IBIs that vary by ecoregion, drainage area, and stream habitat (Ohio EPA 1987~. As men- tioned above, the statistical properties of additive indices make it unreal- istic to add or average scores across spatial scales to create a national indicator (Norris 1995, Gerritsen 1995~. The only effective way to aggre- gate measures into a multimetric indicator is already incorporated into the regulatory policies of the U.S. EPA and state environmental agencies. The Clean Water Act requires that attainment of water quality be reported on the basis of the number of stream miles meeting the criteria. As IBI scores have and will continue to be incorporated into state and federal regulatory standards, attainment will be reported in relation to stream miles assessed.