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a Jornada Exp. Range, MSC 3JER, New Mexico State Univ., Box 30003, Las Cruces, NM 88003, USDA-ARS
b USDA-NRCS, Jornada Exp. Range, MSC 3JER, New Mexico State Univ., Box 30003, Las Cruces, NM 88003
c USDA-NRCS, Oregon State Univ., 202 Strand Agric. Hall, Corvallis, OR 97331
* Corresponding author (jherrick{at}nmsu.edu)
Received for publication May 22, 2000.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONIDENTIFY INDICATORS THAT ARE...BASE INDICATOR SELECTION ON...USE SPATIAL VARIABILITY IN...INTERPRET INDICATORS BASED ON...CONCLUSIONSREFERENCES |
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Abbreviations: SOM, soil organic matter
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONIDENTIFY INDICATORS THAT ARE...BASE INDICATOR SELECTION ON...USE SPATIAL VARIABILITY IN...INTERPRET INDICATORS BASED ON...CONCLUSIONSREFERENCES |
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The concept of soil quality was developed in response to public demand for an increased emphasis on sustainability and to a recognition by many in the scientific community that soil management could be improved by taking a more holistic, integrative approach to soils. These concerns are reflected in SSSA's definition of soil quality: "the capacity of a specific kind of soil to function, within natural or managed ecosystem boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and support human health and habitation" (SSSA, 1997).
The concept of rangeland health was developed in response to similar concerns. Rangeland health is defined as, "the degree to which the integrity of the soil and the ecological processes of rangeland ecosystems are sustained" (Natl. Res. Counc., 1994). Rangeland monitoring and assessment systems have traditionally focused heavily on plant community composition and productivity. Recent interest in rangeland health and a growing recognition of the importance of soil–vegetation feedbacks in structuring rangelands (Schlesinger et al., 1990; Tongway and Ludwig, 1994) have led to a renewed interest in integrating soil information into rangeland monitoring and management.
We have found the following guidelines to be useful in developing integrated soil–vegetation monitoring and management systems for rangelands:
In addition to these guidelines, measurements included in monitoring and assessment systems need to be rapid, simple, inexpensive, and repeatable. To the extent possible, indicators should be predictive: They should reflect early changes in ecological processes and indicate that a more significant change is likely to occur. Each of the four guidelines above is illustrated below using a monitoring system that was recently developed through an informal interagency collaborative effort led by USDA-ARS (Herrick and Whitford, 1999). This monitoring system is designed to detect long-term trends in three attributes: soil and site stability, hydrologic function, and the biotic integrity of the system. Biotic integrity is defined as the capacity of the system to resist and recover from catastrophic disturbance.
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IDENTIFY INDICATORS THAT ARE CONSISTENTLY CORRELATED WITH FUNCTION |
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TOPABSTRACTINTRODUCTIONIDENTIFY INDICATORS THAT ARE...BASE INDICATOR SELECTION ON...USE SPATIAL VARIABILITY IN...INTERPRET INDICATORS BASED ON...CONCLUSIONSREFERENCES |
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We have identified a suite of indicators for use on rangelands based on previously published studies, new research, and expert knowledge about the variability in relationships across diverse rangeland ecosystems. An ongoing research program includes testing and calibrating these indicators directly to ecosystem processes and functions, developing complementary landscape-level indicators, and generating more effective interpretation tools. The indicators are calculated from three core measurements and a number of supplementary measurements (Table 1). Each of the core measurements can be used to generate a suite of indicators, which previous studies have shown are related to each of three attributes or criteria: soil and site stability, watershed function, and biotic integrity (Table 1). The supplementary measurements are applied depending on resource concerns and site characteristics (see below) and are used to generate indicators that generally apply to just one or two of the three attributes.
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The eight supplementary methods (Table 1) have also been correlated with ecosystem function. The belt transect is used for early detection of invasive species, both native and exotic. Many of these species, including mesquite [Prosopis glandulosa Torr.], juniper [Juniperus spp.], Lehmann's lovegrass [Eragrostis lehmanniana Nees], and cheatgrass [Bromus tectorum L.], have been shown to be associated with dramatic changes in soil quality, as reflected in changes in C- and nutrient-cycling processes (Barth and Klemmedson, 1982; Schlesinger et al., 1996; Connin et al., 1997; Arredondo and Johnson, 1999), soil erosion (Davenport et al., 1998), and infiltration capacity (Reid et al., 1999). However, the establishment of these species is often simply an indication that a change has already occurred (Brown and Archer, 1999), significantly reducing their value as indicators. Species richness is a direct measure of the number of species present on a site, calculated using a species area curve based on counts in plots of different sizes (Stohlgren et al., 1995). Plant production using double sampling is normally used for assessment only as it is relatively imprecise and varies dramatically among years.
Two supplementary measurements are specifically designed to measure soil properties that are often closely related to function. The impact penetrometer (Herrick and Jones, 2002) is similar to a standard Corps of Engineers strain gauge penetrometer (Bradford, 1986), except that repeated blows of a 2-kg mass dropped from a standard height replace human force. The use of strain gauge penetrometers is limited by relatively high cost, repeatability problems associated with the need to maintain a constant rate of insertion, and difficulties in comparing data from penetrometers designed for different ranges of soil strength (Fritton, 1990; Vyn and Raimbault, 1993). Furthermore, human strength is often insufficient to use strain gauge penetrometers in dry, uncultivated soils. The impact penetrometer overcomes these limitations. Impact penetrometers can be fabricated by a machine shop for $100, energy is consistently applied, and direct comparisons can be made between measurements made using different drop heights. Drop height is increased in dry, uncultivated soils to reduce the number of strikes and, therefore, the amount of time required. The impact penetrometer does, of course, share several limitations with all penetrometers: Measurements depend on moisture content and cannot be directly related to bulk density. However, it can be easily used to reliably monitor relative changes in compaction over time, provided that moisture content is constant.
The single-ring infiltrometer is similar to that described in Bouwer (1986), except that a constant water depth is maintained by an inverted bottle with an air supply tube inserted to the desired water depth. One of the reasons that the penetrometer and infiltrometer are listed as supplementary methods is that the relationships to ecosystem functions appear to be inconsistent in some plant communities, such as blue grama [Bouteloua gracilis (H.B.K.) Lag] grasslands. For example, penetrometer resistance often increases under blue grama grass canopy due to the high root density near the soil surface (Table 2). The final set of supplementary measurements apply specifically to riparian and woodland systems: riparian channel vegetation survey and channel survey and tree density.
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BASE INDICATOR SELECTION ON RESOURCE CONCERNS AND SITE CHARACTERISTICS |
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TOPABSTRACTINTRODUCTIONIDENTIFY INDICATORS THAT ARE...BASE INDICATOR SELECTION ON...USE SPATIAL VARIABILITY IN...INTERPRET INDICATORS BASED ON...CONCLUSIONSREFERENCES |
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Given that societal values, as well as scientific understanding, change over time, monitoring programs should be designed to quantify the potential of the system to (i) function in support of a range of societal values rather than to support any individual value, (ii) resist degradation, and (iii) recover following degradation. The premise that the capacity of an individual site to function depends on a core set of processes is common to most definitions of both soil quality and rangeland health. The core measurements were selected to generate indicators of these processes. Indicators that address specific values or land uses, such as livestock forage or wildlife habitat, can often be calculated from the core measurements (Table 1), and additional measurements can be included. In rangelands, however, we have found that it is quite useful to maintain a distinction between core and supplementary measurements to minimize unnecessary cost increases. The primary criterion for indicator selection must be the strength and consistency of its relationship to a critical process while recognizing that the relative importance of different ecological processes, and the strength of the relationship between indicator and process, varies among soils, landscape positions, and regions. In some cases, relationships between indicators and processes can be inferred from the literature while in others, they must be quantified with new studies.
Termites illustrate how the relationship between an indicator and a property can vary at both the landscape and regional scale due to differences in the relative importance of processes that the termites affect and the relative effects of termites on those processes. Termites are often proposed by progressive land managers as a valuable biological indicator in the southwestern USA, particularly after the managers learn of termites' contribution to dung decomposition, nutrient cycling, and macropore formation.
Removal of termites from plots on a Chihuahuan Desert bajada resulted in a 42% reduction in saturated infiltration rate in plant interspaces (88.4–51.3 mm h-1) 4 yr after termite exclusion (Elkins et al., 1986). A similar experiment completed in West Texas generated opposite results. The depth of water infiltrating the soil over a period of 40 min was 23% higher in termite removal plots (18.5 mm) than in controls (15 mm) 2 to 3 yr after termite exclusion (Spears et al., 1975). The explanation provided by the authors was that in the Chihuahuan Desert study, macropore formation was the dominant process affecting infiltration. In West Texas, litter cover was more important. Termites are responsible for removing large amounts of litter and reducing soil organic matter (SOM) in both systems (Nash and Whitford, 1995). In the Texas study, litter increased from 35 g m-2 in the control plots to 63 g m-2 in the termite removal plots. Soil organic C in the surface 1 cm correspondingly increased from 1.2 to 1.8%. This illustrates that the use of an individual organism as an indicator of a property, such as infiltration capacity, can be complicated if the organism affects more than one process related to the property. This also illustrates how relative differences in the importance of different processes can confound indicator interpretation across regions.
Termites are also difficult to use as indicators because their populations vary with soil type and landscape position. In the Chihuahuan Desert, the relationship between recent termite activity and infiltration capacity is potentially high in the uplands but nonexistent in the playas, from which termites are largely absent (Nash and Whitford, 1995). The contribution of termites to infiltration and other ecosystem processes also declines with increases in latitude and elevation due to the changes in species composition (Weesner, 1965; Herrick, 1999).
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USE SPATIAL VARIABILITY IN DEVELOPING AND INTERPRETING INDICATORS |
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TOPABSTRACTINTRODUCTIONIDENTIFY INDICATORS THAT ARE...BASE INDICATOR SELECTION ON...USE SPATIAL VARIABILITY IN...INTERPRET INDICATORS BASED ON...CONCLUSIONSREFERENCES |
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Changes in spatial variability that reflect changes in process and function can occur at any one of a number of scales. The example illustrated by Fig. 1 is commonly used because its relationship to function has been relatively well described for a number of systems (Schlesinger et al., 1990; Pierson, et al., 1994; Reid et al., 1999). Spatial variability at finer scales, such as surface roughness, can be a useful indicator of susceptibility to erosion (Saleh and Fryrear, 1999). At coarser scales, changes in the relative size of vegetation patches, erosional areas, and depositional areas and shifts in ecotonal boundaries can be sensitive indicators that change is occurring throughout the landscape (Coffin and Lauenroth, 1990; Ludwig and Tongway, 1995).
Because they are related to processes, spatial indicators are often particularly useful for making management decisions. Management is often designed to effect a short-term change in a property of a system, such as bulk density or infiltration capacity, but it is the long-term change in a process, such as redistribution of water, that has the greatest effect on the system.
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INTERPRET INDICATORS BASED ON AN UNDERSTANDING OF ECOLOGICAL PROCESSES |
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TOPABSTRACTINTRODUCTIONIDENTIFY INDICATORS THAT ARE...BASE INDICATOR SELECTION ON...USE SPATIAL VARIABILITY IN...INTERPRET INDICATORS BASED ON...CONCLUSIONSREFERENCES |
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The threshold concept is widely applied in agronomy and land management, particularly in the areas of integrated pest management (Kogan, 1998; Hoffman et al., 1999), landscape stability, and soil erosion (Davenport et al., 1998; Weltz et al., 1998). The concept has been applied in rangeland ecosystems in the form of state and transition models (Westoby et al., 1989; Friedel, 1991). These models are based on the assumption that relationships between different properties and processes become increasingly nonlinear as a threshold is approached. The application of these models is a relatively recent development and represents a significant departure from the linear plant succession–regression based paradigm that guided range management through most of this century (Soc. for Range Manage., 1995). The fact that the process–property relationships become increasingly nonlinear near thresholds means that linear combinations of indicators that effectively reflect changes in ecosystem function may become completely ineffective at these critical periods.
A unique state and transition model can be described for each soil or suite of similar soils. State and transition models consist of states, transitions, and thresholds. A state can be defined by a single plant community or multiple plant communities together with characteristic dynamic soil properties, such as organic matter content and erodibility. Although the dominant species in each of the plant communities are used to conveniently describe the states, the state is defined by soil and vegetation properties and processes and by soil x vegetation interactions mediated by the animal community. Soil and vegetation changes within a state are easily reversible, and the states themselves are relatively stable. Transitions between states occur after crossing a soil- or vegetation-defined threshold that is not easily reversed without significant inputs of resources (Friedel, 1991; Committee on Rangeland Classification, 1994).
The state and transition diagram illustrated in Fig. 2 is based on current understanding of the ecological dynamics on a northern Chihuahuan Desert site with soils that are shallow (<60 cm deep), sandy, and have a petrocalcic horizon, as represented by the Simona soil (loamy, mixed, thermic, shallow Typic Paleorthids). States 1 and 2 are dominated by black grama [B. eriopoda Torr.], a C4 stoloniferous grass, and have few large canopy interspaces, except those generated by small-scale disturbances such as banner-tailed kangaroo rat [Dipodomys spectabilis] mounds. Use of these mounds by the rats prevents the establishment of perennial plants. Except for the rodent mounds, the soil is relatively stable in State 1, and there is a clearly defined A horizon evenly distributed throughout the site, reflecting relatively uniform resource distribution. Microbiotic crusts dominated by cyanobacteria are common. A black grama–bunchgrass community can also exist in this state.
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The transition from State 1 or 2 to State 3 is defined by mesquite seed dispersal and establishment. Mesquite is a leguminous shrub. The relative importance of soil surface degradation and mesquite invasion in defining the transition to State 3 has never been clearly defined because the two processes have historically occurred simultaneously in many areas: A combination of overgrazing and drought have exposed the soil surface to erosion, and livestock have dispersed large quantities of mesquite seed into grasslands. Evidence from a more mesic system in Texas suggests that seed dispersal may be the limiting factor (Brown and Archer, 1999). Reversal of this transition requires elimination of the mesquite, its seedbank, and dispersal agents and possibly restabilization of the soil surface.
The third state includes at least two communities. In addition to the presence of mesquite, the state is characterized by larger intercanopy gaps. The core measurements listed in Table 1 were completed at sites that are representative of States 3 and 4. The quantitative indicators are summarized in Table 4. Both sites are located on the Simona soil in southern New Mexico on the USDA-ARS Jornada Experimental Range where precipitation from 1961 to 1994 averaged 261 mm. As the transition from State 3 to State 4 begins, soil aggregate stability in the plant interspaces continues to decline, SOM distribution becomes increasingly concentrated under plants, and canopy gap diameters increase (Tiedemann and Klemmedson, 1986; Wright and Honea, 1986; Schlesinger et al., 1990) (Tables 1 and 4).
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This diagram (Fig. 2) is relatively simple: Community pathways branch within states, but there are at most two potential transitions for each state. The structure is based on that by Stringham et al. (2001). The number of potential states could easily increase in response to climate change or species invasions. Earlier state and transition models often recognized individual communities as separate states, whether or not they were separated by thresholds, making it difficult to consistently define states.
The key points illustrated by this example are that transitions are defined by nonlinear changes in the function of the system and that mean values of a suite of indicators may provide relatively little information about the status of the system when it is near threshold. The transition from State 1 to State 3 can be precipitated by the mere establishment of mesquite in the system. An average of all other indicators may show little or no change. Similarly, the fact that the site described under State 3 in Table 4 is near, if not at, threshold would not be predicted from an average value of the quantitative indicators, nor from a casual visual examination of the site or a more formal qualitative evaluation. With the exception of small, scattered mesquite shrubs, the site appears to be similar to a grassland site in State 1, and only 2 of 17 qualitative vegetation and soil surface indicators used to evaluate the site (Pellant et al., 2000) were rated by an interdisciplinary team of experts as being more than moderately different from what would be expected for a site in State 1. This qualitative evaluation system uses five categories to describe the relative difference between the site of interest and a real or (in this case) reconstructed reference site: none–slight, slight–moderate, moderate, moderate–extreme, and extreme. The two qualitative indicators that were rated as being more than moderately different from what would be expected for the site in State 1 were the degree of pedestalling and soil surface resistance to erosion. The former indicated a large amount of historic soil redistribution; the latter indicated that the soil is currently highly erodible. A third indicator, plant functional and structural groups, was rated by the interdisciplinary team as being only slightly to moderately different from what would be expected for State 1 because species representing most of the original functional and structural groups were present on the site and there was only one new group: that represented by mesquite. Although mesquite was not recorded on the two 50-m line transects (Table 4), there were a number of plants on the site that were too large to be effectively killed by fire or rodent activity. This, together with a highly eroded and erodible surface and the incipient development of large gaps in the canopy, indicate that this site is at or near threshold, in spite of the fact that the majority of both qualitative and quantitative indicators suggest that it is in relatively good condition compared with a site in State 4 (Table 4).
The precise definition, quantification, and recognition of site-specific thresholds are some of the most important and difficult tasks faced by the range management community. Economic criteria similar to those applied in integrated weed-management systems can be used to more clearly, albeit artificially, define thresholds: the point at which external inputs are no longer recovered by increased production of the former state. This is qualitatively different than the earlier threshold most commonly defined in integrated weed-management programs: the point at which pest populations reach a level beyond which the cost of additional reductions in crop production in the absence of control exceeds the cost of the of the control measure. This type of threshold can be identically applied to weed management programs in rangelands and, if applied, may prevent the system from reaching a second threshold beyond which control is no longer economically viable and the system moves into a new state. In general, however, ecologically defined thresholds are preferable to economically defined thresholds for describing ecosystems because the definitions are less likely to change over time with economic conditions.
There are a number of other studies in the literature that also demonstrate the importance of thresholds, and the most effective early warning indicators are not necessarily the most obvious indicators. For example, herbaceous species composition and productivity are commonly considered to be good indicators of condition. However, Northrup and Brown (1999) illustrated that while a tussock grass–dominated community appeared to be stable based on these indicators, loss of biological integrity in response to overgrazing was occurring via the fine-scale redistribution of soil resources. In another example, Brown and Ash (1996) showed that monitoring species composition and plant production data to detect shrub invasion can result in delaying important management decisions until well after woody invaders have become established. Instead, indicators that indicate susceptibility to invasion need to be identified. Finally, Davenport et al. (1998) clearly demonstrated the role of erosion thresholds in defining different states for grasslands invaded by piñon and juniper in the Southwest and documented that the probability of crossing different thresholds strongly depends on site characteristics.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONIDENTIFY INDICATORS THAT ARE...BASE INDICATOR SELECTION ON...USE SPATIAL VARIABILITY IN...INTERPRET INDICATORS BASED ON...CONCLUSIONSREFERENCES |
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ACKNOWLEDGMENTS |
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REFERENCES |
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TOPABSTRACTINTRODUCTIONIDENTIFY INDICATORS THAT ARE...BASE INDICATOR SELECTION ON...USE SPATIAL VARIABILITY IN...INTERPRET INDICATORS BASED ON...CONCLUSIONSREFERENCES |
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in southern Otero County, NM, USA. Data reflect number of strikes required to drive a 2-kg mass dropped from a height of 60 cm to drive a standard Corps of Engineers penetrometer 5 cm into dry soil at each of three depths.
