Manejo/Gestión

The Clean Water Act (CWA) of 1972 is the instrument by which water quality is protected within the United States. Through court decisions, the interpretation of this legislation has changed over time (Adler 1999; Downing et al. 2003; Murphy 2006).

However, the CWA is generally considered to be effective in maintaining water quality, ensuring anti-degradation of water, and in slowing the rate of wetland loss.

The basis for the CWA focuses on “maintaining the chemical, biological, and physical integrity of waters within the United States.” As this pertains to wetlands, the CWA has evolved into a process and structure by which the destruction of wetlands due to anthropogenic impacts must be mitigated, through either the creation of new wetlands, or the restoration of degraded ones. The wetlands created or restored as a result of mitigation have replicated the natural wetlands which they replaced with mixed success (Balcombe et al. 2005a,b,c; Brown and Veneman 2001; Cole and Brooks 2000a; Perry et al. 1996). However, as more research is being devoted to understanding the role

wetlands play within a landscape, the prospects for successful mitigation are increasing (Brooks et al. 2005; Mitsch and Wilson 1996). Reference wetlands with minimal human impact are being used to evaluate mitigation success of wetlands with a landscape context (Bedford 1996; Brinson and Rheinhardt 1996). However, the true ecological success of these mitigated sites remains relative and subjective. For instance, mitigated wetlands are well-documented in improving water quality (Fleming-Singer and Horne 2006; Kovacic et al. 2006; Poe et al. 2003; White and Bayley 1999). However, the restoration of the biological flora and fauna, as well as the hydrologic and physical characteristics compared to natural wetlands, is questionable. Soil characteristics and hydrology of

created wetlands are typically wetter than natural wetlands (Cole et al. 2006; Cole and Brooks 2000b), and lack variation in microtopography (Bruland and Richardson 2005;

Stolt et al. 2000). Vegetation development and structure differ between natural and mitigated wetlands (Balcombe et al. 2005a; Brown and Veneman 2001); but this is not necessarily indicative of a mitigated wetland not performing the same ecosystem functions as natural wetlands (Wilson and Mitsch 1996). As may be expected, if differences in vegetation communities are not uncommon, neither are differences in invertebrate communities (Balcombe et al. 2005b; Stanczak and Keiper 2004), or avian (Brown and Smith 1998; Snell-Rood and Cristol 2003) and anuran assemblages

(Balcombe et al. 2005c).

The process of determining whether wetland integrity is compromised has historically been through monitoring water chemistry. However, protecting wetlands in this manner does not ensure that the physical or biological integrity is being maintained.

Chemical measurements are evidence of the condition at a point in time and the cumulative biotic effects of the chemical stressors may not be evident. Measuring physical parameters of a wetland can also overlook biological and chemical stressors affecting a system (Karr and Chu 1999; Yoder and Rankin 1995). Within the current federal wetland policy, despite the CWA mandate to protect water quality, wetland function and biotic integrity can be compromised from anthropogenic impacts in proximity to the wetlands (Harris 1988; Winter 1988; Yuan and Norton 2004). The functions that wetlands provide (e.g., the transfer and storage of water, production of plants and animals, biochemical transformation and storage, decomposition of organic materials, and provision of habitat) (Ehrenfeld 2004; Richardson 1994), occur on

multiple spatial scales within a matrix of landscapes (Zedler 2003). Therefore evaluating the impacts and stressors that can influence wetlands also needs to be evaluated over time on a landscape basis (Bedford and Preston 1988; Hemond and Benoit 1988; Risser 1988;

Whigham et al. 1988).

As the tools used to interpret and implement the CWA mandates have evolved, indices of biotic integrity (IBIs) have emerged as a cost-effective way of measuring the biological integrity of multiple systems both domestically and internationally (Karr and Chu 2000; Karr 1991; Miltner et al.2004; Moyle and Randall 1998; Simon et al. 2000;

Teels et al. 2004; Veraat et al. 2004). Metrics, or biological attributes that respond minimally to natural disturbance while responding in a predictable and consistent manner to human impairment, are used to form IBIs. Biological integrity is specifically and operationally defined as the state of biota in systems with minimal human disturbance (Jackson and Davis 1995; Steedman 1995). A central premise to integrity is the assumption that all biological systems evolve towards a product of self organization resulting in community structure as a function of both positive and negative feedback (Campbell 2000). Community structure requires a prescribed amount of energy to maintain itself. With significant impacts via human impairment, the energy required to maintain this structural integrity is no longer attainable. As the system adapts, the shift will be represented by changes in biotic structure (Klopatek 1988). Changes in biotic structure should not be confused with differences in species’ abundances and

distributions due to differing wetland types (Brinson 1988), so a hierarchal approach to biological assessments that evaluates the community and population dynamics, within a regional landscape context, is best to detect losses in wetland function and regional

biodiversity (Noss 1990). Deciphering the impairments of wetlands at multiple scales is important when seeking an understanding of open systems (Jacobson 2000); however, caution must be taken to consider apropos variables that are the stressors rather than symptoms or by-products of stressors. For example, the percent of impervious surface is the stressor, whereas roads and development are symptoms of the stressor (Brooks et al.

1998; Novotny et al. 2005).

A critical component in developing an IBI is the identification of an effective disturbance gradient that is sensitive enough to exhibit multiple levels of human disturbance (Mack, 2005, personal communication; U.S. EPA 2002). Local-level disturbance indices that require a site visit for assessment have been developed and used in Pennsylvania, Ohio, Minnesota, and Delaware to compare site-specific disturbance scores to biological attribute metric scores (Brooks et al. 2006;Helgen and Gernes 2002;

Jacobs 2006; Mack 2001). Although each state has developed a disturbance assessment procedure, they are all based in-part on wetland stressors drawn from literature (Adamus and Brandt1990). In some of the above-mentioned states, the site level stressor gradient is augmented by data from spatial features to increase sensitivity of the disturbance index (Brooks et al. 2006). Using a geographic information system (GIS) is more cost-efficient than individual on-site visits (Brooks et al. 2004). Using only GIS derived data, a

Landscape Disturbance Index (LDI) has served as the disturbance gradient in assessing human impairment (Brown and Vivas 2005). However, on-site assessments are generally more effective in demonstrating significant relations and explaining a greater part of the variability associated with metrics (Micacchion 2004).

States have developed IBIs for wetlands using multiple assemblages of species including algae, plants, fish, macroinvertebrates, and birds (U.S. EPA 2002). By sampling multiple taxonomic groups there can be numerous candidate metrics from which to evaluate impairment to better understand the full complexity of wetland systems (Dale and Beyeler 2001; O’Connor et al. 2000).

Wetlands are commonly classified by vegetation structure (Cowardin et al. 1979) and will be referred to as the “Cowardin” classification method in this document. The Cowardin classes have been demonstrated to be an effective categorization for an

amphibian-based IBI in Ohio wetlands (Miccachion 2004). This scheme groups wetlands as emergent (EM) (Figure 1), scrub-shrub (SS) (Figure 2), and forested (FO) (Figure 3);

and is used in mapping by the National Wetland Inventory (NWI). An alternative to using vegetation to classify wetlands is the hydrogeomorphic (HGM) approach (Brinson 1993). The HGM classification resolves many of the shortcomings of the Cowardin approach. For example, in the Cowardin classification system a palustrine emergent wetland may be found along a river floodplain, fringing a lake, or as a prairie pothole; all of which are functionally dissimilar (Stevenson and Hauer 2002). The HGM approach is based on physical determinants of wetland structure and function, according to the geologic setting and hydrologic regime; therefore, allowing the aggregation of wetlands that are functionally similar (Smith et al. 1995).

When interpreting biological studies within wetlands it is necessary to think in terms of the influence that climate and hydrologic settings have on biological

communities. This continuum is most easily thought of as a two-dimensional gradient represented by groundwater and atmospheric water. By locating the position of any

wetland along both axes of the continuum, the potential biological expression of the wetland community can be predicted (Euliss et al. 2004). However, determining this point of hydrologic variability for wetlands is difficult and can complicate matters when attempting to apply and interpret it in relation to an IBI (Wilcox et al. 2002). Appropriate classifications, especially relative to hydrologic regimes, are essential to developing an effective IBI (Karr and Chu 1999). By classifying wetlands according to HGM

subclasses, the subclasses themselves can be used as surrogate categorical variables to characterize hydrologic variability (Cole and Brooks 2000b; Cole et al. 1997; Merkey 2006). The coupling of the HGM approach of classifying wetlands with the IBI approach for measuring wetland impairment has been called for to increase the effectiveness and sensitivity in detecting disturbance (Stevenson and Hauer 2002). This technique

achieved success in North Carolina (Rheinhardt et al. 1999) and Pennsylvania (Brooks et al. 2006).

In developing IBIs or bioassessments, stratification by ecoregion is important to reduce variance in the final product (Klopatek 1988; Omernik 1995). In some cases, indices can be sufficiently robust for use in multiple ecoregions (Hill et al. 2003;

McCormick et al. 2001); however, multiple IBI standards have been developed to account for detectable, predictable, ecoregion variation (Mack 2001). Level 3 ecoregions (Omernik 1987) are the level of resolution used in existing regional IBI programs (Mack 2004; Micacchion 2004; Miller et al. 2006; Miller et al. 2004).