- Email: [email protected]
Summary and Conclusions
Summary and Conclusions Paul Adamus*, John Dorney†, Ralph W. Tiner‡ and Rick Savage§ *Oregon State University, Corvallis, OR, United States, † Moffatt and Nichol, Raleigh, NC, United States, ‡ Institute for Wetland & Environmental Education & Research, Leverett, MA, United States, § Carolina Wetlands Association, Raleigh, NC, United States
Rapid assessment methods (RAMs) are standardized procedures that generate a score, index, or rating for a specified site (individual wetland, stream, watershed, etc.) and/or for its individual ecosystem services (functions, values) or other attributes (e.g., condition, sensitivity), based mainly on ground-level observations and/or by using aerial imagery/GIS as described in this book. The observational nature of RAM procedures contrasts with procedures based mainly on ground-level measurements, such as kilograms of nutrients removed by a wetland or the diversity of the aquatic community in a stream. If a site visit is needed, RAMs commonly require just one such visit lasting less than 1 day. Most RAMs do not require a comprehensive knowledge of plant, insect, or soil taxonomy. This book will help government scientists or others when they are tasked with developing a wetland or stream RAM that is optimized for a particular region, modifying and calibrating an existing RAM from another region so that it is optimal for the user’s region, or calibrating and verifying an existing RAM. The book also will help RAM users make better use of wetland and stream RAMs as they gain a deeper understanding of how RAMs have been developed, tested, and applied. Section and chapter introductions describe many of the lessons we, as editors, have learned from developing RAMs in various regions. Additional insights on how wetland and stream RAMs have been developed and applied can be gained by reading this book’s case histories. RAM development has largely been spurred by a desire for systematic tools to help evaluate impacts to wetlands and streams from development, and to achieve appropriate mitigation based on both the quality and quantity of the resource. RAMs provide key information to those responsible for making decisions about development or conservation of wetlands and streams. RAMs characterize individual stream or wetland sites and may compare them with others. They do so for evaluating project impacts, designing appropriate mitigation, and/or developing strategies that promote conservation. RAMs provide vital information for decision making, but most jurisdictions do not require that RAM results be the sole determinant of a decision about a wetland or stream reach. Many RAMs are localized or regional adaptations of a general template developed previously, such as the hydrogeomorphic (HGM) assessment framework (Smith et al., 2013), the WESP template (Adamus, 2016, Chapter 4.3.2), the Ohio Rapid Assessment Method (Chapter 4.3.8), or the LLWW and NWI-Plus templates for landscape-level assessments (Tiner, 2003; case studies in Section 2.0). RAMs for assessing wetlands are more common than those for streams. Many RAMs are being refined and adapted for use in new geographic areas as well as in response to expanding scientific knowledge, technology, software capabilities, and spatial data availability. In the United States, the impetus for developing wetland and stream RAMs was the Clean Water Act of 1972—the keystone for wetland regulation across the country. Most of the published RAMs have been sponsored by North American states and provinces and are intended to enhance the incorporation of scientific knowledge and existing spatial data into regulatory decisions affecting wetlands and streams. Nearly all RAMs provide one or more scores and/or ratings for an individual site or a larger landscape by using models to process a user’s standardized data inputs. Most often, the models are mathematical formulas that the RAM developer has constructed based partly on published science and partly on correlations thought to exist between various conditions of several indicator variables and an endpoint (wetland or stream functions, values, and/or ecological condition). Some models consist of narrative criteria rather than formulas, and some RAMs do not include explicit models but rather leave it to the user’s judgment to connect the user’s observations of indicator variables to a rating or score intended to represent the endpoint. For only a very few RAMs have the sponsors attempted to validate the presumed correlations between indicators and endpoints by making detailed measurements of the endpoints and comparing those measurements with model outputs to determine if sites are ranked similarly. More often, RAM models are simply verified by comparing the ranking of a series of sites based on model outputs with a ranking based on the independent opinions of local experts on each function 551
552
Summary and Conclusions
or status condition. The repeatability (consistency) of RAM outputs among independent users visiting the same series of sites has apparently been tested in only a few instances. As reflected in the structure of this book, wetland and stream RAMs are commonly developed as either landscape-level or field-level methodologies. Also, based on what they aim to assess, they are often characterized as either functionassessment RAMs (which may also assess the values of those wetland or stream functions as related partly to ecosystem services) or condition-assessment RAMs (which address specific values such as wetland or stream health, quality, status, or integrity). Several RAMs incorporate combinations of these approaches and themes. Condition-assessment RAMs cannot consistently predict all or most functions, and function-assessment RAMs cannot reliably predict the condition of wetlands or streams as it is conventionally defined. Landscape-level wetland RAMs use remotely sensed imagery, existing spatial data layers, and GIS combined with existing knowledge of wetlands to compile input data for models that largely resemble those used in field-level RAMs, and similarly produce scores or ratings believed to reflect wetland or stream functions and/or condition. At present, landscape-level stream RAMs are rare to nonexistent. Landscape-level RAMs are mainly employed when a goal is to assess all wetland or stream sites across a large area (e.g., watershed, river basin, ecoregion) because cost and time requirements to visit and assess all those sites would be prohibitive, even if all sites were accessible for examination. This type of RAM also is an attractive tool for assessing stream segments or wetlands where field-based RAMs have not been developed, or where use of field-level RAMs is difficult or impossible because sites are too remote or hazardous to access, are too large to assess meaningfully during a single 1-day visit, or are inaccessible due to resistance from land owners or managers. Landscape analysis is a first-step, basic assessment derived from interpretation of imagery and maps and existing knowledge of the variety of wetlands that occur across a geographic region. Such analyses can be done any place with suitable imagery where wetlands or streams can be detected and where the relationship between interpreted variables (such as dominant vegetation, hydrology, landscape position, and connectivity) and wetland function or condition is reasonably understood. As mentioned, landscape analyses provide a preliminary assessment as they are limited by source data (aerial imagery and/or GIS data/maps) for a number of reasons, including: (1) not all wetlands and small streams are readily identified on aerial imagery, (2) scale issues (e.g., limitations to the smallest size of wetland or stream that can be identified remotely and displayed on a map), (3) misidentification of an upland as a wetland and inclusions of upland in wetland units and vice versa, (4) misclassification of wetland type, and (5) registration issues (i.e., positional accuracy). Moreover, many important indicator variables (e.g., plant community composition, soil texture, downed wood, microtopography, high water marks on vegetation, concealed outlets, water quality parameters, aquatic macroinvertebrate communities) can be reliably detected only during a site visit and so are lacking in landscape-level RAM models. Consequently, landscape-level RAMs are usually intended for gaining a broad perspective on wetland and stream functions and condition across a watershed or larger geographic area (e.g., analyzing cumulative impacts of development on wetland functions), developing conservation strategies, general planning purposes, and educating the public on expected functions and condition, rather than for making regulatory or management decisions about individual wetlands or project areas. The accuracy of landscape-level assessments can be improved by conducting field investigations to verify wetland and stream classifications and the interpretations of wetland and stream functions or condition. In Alberta, the rank order of a series of wetland sites scored by a field-level RAM was compared with the rank order of the same sites scored independently by a landscape-level RAM, and little concordance was found in the scores or ratings of several functions. An example of the mutually reinforcing effect of landscape-level and field-based analyses is described by work done on geographically isolated wetlands in North and South Carolina (RTI International et al., 2011). The goal of this work was to derive a statistically valid estimate of the extent and condition of isolated wetlands in an eight-county study area in these two states. First, a GIS map was prepared based on a wide variety of GIS layers. Then, a stratified random sample of sites was chosen and field work conducted to determine if the sites were isolated wetlands and, if so, to evaluate their condition using NC WAM (Chapter 4.3.1). Based on this field evaluation, it was determined that the GIS map correctly identified wetlands 69% of the time but only correctly identified isolated wetlands 22% of the time. This high rate of misidentification of isolated wetlands had two main causes: (1) many small ditches were present in the field but not discernable from GIS layers, which then made the sites not isolated, and (2) many sites had been falsely identified as wetlands but were either upland or ponds. However, because the field sample was statistically developed, the authors were able to use data from the GIS map and the NC WAM results to make definitive conclusions about the extent of isolated wetlands in the study area as well as their condition. The combined use of a landscape-level model followed by statistically valid field-level investigations provides a good conceptual model to take advantage of the strength of both these approaches and to minimize the weaknesses of both these approaches. Landscape analysis is often part of field-based RAMs because one cannot see everything from the ground or take time to traverse the entire project area. In addition, landscape-level analysis is often used to address broader watershed or wetland
Summary and Conclusions
553
buffer issues that influence the functioning or condition of a wetland or stream. In general, results from the field-level RAM are preferred over landscape-level assessments because they incorporate many more indicators than landscape-level assessments and involve close-up inspection and verification. However, because ground-level field visits are often physically hazardous, are limited to a 1-day visit, and time-consuming for multiple wetlands, use of remote sensing and GIS analysis can be more accurate for predicting some functions of very large sites or those with poor access. In addition, calibrating a field-based RAM prior to its being made available for public use requires significant time and effort. Of the dozens of field-based RAMs developed and calibrated over the past four decades, only a relatively small number are still used regularly. Why is that? A primary determinant of sustained use is the degree to which sponsors require the use of a particular RAM legally or as official agency policy, or at least unofficially encourage its use (Arnold, 2012, 2014, 2015). Degree of use also depends on the sponsor’s outreach efforts, lack of other RAMs that have been calibrated to the specific region and/or wetland type being assessed, the RAM’s ease of use, the availability of low-cost training for the RAM’s potential users, and the ability of sponsors to invest in continued refinement of the RAM in response to the availability of improved spatial data and imagery, new scientific findings, wider peer review and verification, expanded repeatability testing, and validation by comparison with more intensive measures of function and condition. RAM sponsors must decide how frequently to release revisions and updates so as to avoid creating a “moving target” that can frustrate users if a site’s rating changes over time as a result of a sponsor’s well-intended revisions of the RAM. As agency budgets and availability of qualified staff decrease, RAM developers and their sponsors will likely find themselves pressured to make their RAM shorter, faster, and simpler. Can this be done while still achieving sufficient accuracy and achieving a desired application outcome such as avoidance or minimization of impacts? Often the requests for RAM streamlining originate from the regulated community out of a desire to minimize project costs by shortening the time required to do an assessment. In some instances limited streamlining is possible without compromising the scientific integrity, sensitivity, repeatability, or accuracy of the RAM, but regulatory agencies with input from scientists must decide the extent to which this is possible. While most RAMs have been developed by regulatory or natural resource agencies for specific uses in specific areas, the scientific assumptions their models employ to assess what seems to be the same thing (e.g., a particular function) sometimes differ among RAMs. For example, some wetland RAMs assign highest scores for water storage function to river-associated sites whereas others assign the highest to depressional (closed basin, isolated) wetlands. This could be due to differing definitions of the water storage function (especially whether a site’s area is included in the score calculation), a confusion of functions with values, a misunderstanding of physics and wetland science, or regional differences in how wetlands function. Better-documented rationales and broader consensus need to be sought concerning each of the hundreds of correlations assumed between various functions and their indicator variables. Support is warranted for the development or regional adaptation of templates for assessing wetlands in states, provinces, and nations that currently lack wetland or stream RAMs. Even more pressing is the need for developing or adapting RAMs applicable to the functions of stream and riparian segments outside the Mid-Atlantic and Pacific Northwest portions of North America, and RAMs for assessing functions of aquatic habitats other than wetlands and streams. For many RAMs, more effort should be made to incorporate knowledge of indigenous peoples when identifying and modeling suitable indicators of functions, values, and site condition. The potential for specifying the use of emerging technologies (e.g., drones, sensors, software, and modeling techniques) in RAMs should be explored fully with a goal of producing more consistent and accurate results at less cost. Better scientific information is needed with regard to how to quantify, quickly and accurately, the short and long term as well as cumulative effects of various types of stressors on site functions using only onevisit field observations and conventionally available imagery. As described in Section 3.0, most wetland and stream RAMs focus either on assessing site functions or assessing site health (condition or quality), and these two are not synonymous. Additional research is warranted to determine under what conditions (which functions, regions, wetland types, and metrics) the RAM-generated predictions of wetland health match RAM-generated predictions of each wetland function, and vice versa. This may increase confidence that the results of using one or the other type of RAM adequately represent what is assessed by the other. The term “tested” is widely used by RAM authors hoping to inspire confidence among users, yet a review of existing RAMs suggests it can mean anything from casual examination of RAM results from a few wetlands by its author to correlation with results from other RAMs to rigorous comparison of results with direct, long-term measurements of function across a large enough series of sites verified to statistically encompass the variation of conditions present in a region. A need exists for measuring the repeatability of many RAMs (and, especially, using the resulting confidence intervals around the scores to ensure the correct use of the scores for interpreting functional “lift” or impact over time). As budgets and time allow, more effort should especially be devoted to verifying and validating parts of the many RAMs currently in use as well as measuring the repeatability of RAM scores and ratings among independent users assessing the same sites.
554
Summary and Conclusions
Guidance is needed from statisticians for appropriate ways of determining the adequate number and type of sites for field calibrating a RAM, and for evaluating under what conditions a RAM can be validly extended into regions where it was not field calibrated. With such guidance, the adequacy of the number of sites where existing RAMs have been calibrated should be examined to determine if more calibration sites need to be added in each RAM’s focal region. Adding more calibration sites within any region will allow existing RAMs to be calibrated more precisely by allowing the RAM’s scores to be normalized by specific wetland types or settings. Thus, a rating of “high,” for example, could represent a high level of a particular function or condition relative to other sites of that wetland type, or relative only to (say) other wetlands in an urban setting rather than relative to all wetlands within the RAM’s region regardless of type or setting, which is often the case with existing RAMs. What then is the future of RAMs? Based on the work that the authors have done and the chapters included in this book, we foresee several trends and make related recommendations: (1) The work to validate RAMs will continue and, we recommend, should be accelerated. We recommend that government agencies (and other funding sources) recognize the importance of this effort and adequately fund this work. In the case of the United States, we acknowledge the importance of the U.S. EPA’s Wetland Program Development Grant Program and suggest that it explicitly list validation of both function and condition RAMs as a funding priority. (2) RAMs for streams and other ecosystems will be more common in the future. In the United States, this seems like a logical result of the joint mitigation rule of the U.S. Army Corps of Engineers and U.S. EPA (US Environmental Protection Agency and US Army Corps of Engineers, 2008). We suggest that these agencies prioritize the development and testing of regional RAMs. In addition, the administrators of these agencies should prioritize implementation of the joint mitigation rule by their staffs. (3) Despite a growing need, RAMs in developing countries will largely continue to be based on professional judgment due to scarcity of scientific data, spatial data, and skilled professionals. Improving wetland inventories for application of landscape-level approaches should be the first step in wetland assessment for large geographic regions, providing the broad overview of wetlands and their functions for strategic conservation planning. We suggest that international organizations (such as the United Nations and the Ramsar Convention) make development and testing of RAMs in developing countries a high priority. A significant role may be played by regional organizations such as the Organization of American States for RAM development in Latin America because RAMs are apparently very sparse in this region. As a parallel effort, we suggest that scientific organizations (such as the Society of Wetland Scientists and the Society of Freshwater Scientists) make outreach and assistance to these countries to develop and test RAMs a priority. (4) RAMs that require “relatively undisturbed” reference sites to calibrate their scores will need to consider other ways to establish reference condition in highly developed landscapes, such as many in Europe, India, and China. Given contemporary environmental conditions, the most practical reference sites may just have to be those that seem to be subjected to the fewest current stressors, rather than pristine sites (Stoddard et al., 2006). (5) The outputs of RAMs will be more explicitly incorporated into regulatory programs as the value of the results of these RAMs becomes more apparent to the regulated community, agencies, and the general public. To accelerate this process in the United States, we suggest that organizations such as the Association of State Wetland Managers and agencies such as the U.S. EPA and the U.S. Army Corps of Engineers expand their current efforts and funding for explaining the utility of wetland and stream RAMs to the general public. More funding for updating and enhancing the U.S. Fish and Wildlife Service’s National Wetlands Inventory (NWI) will make NWI data more useful for landscape-level wetland function assessments across the country.
REFERENCES Adamus, P.R., 2016. Manual for the Wetland Ecosystem Services Protocol (WESP). Version 1.3. Internet: people.oregonstate.edu/adamusp/WESP. Arnold, G., 2012. Assessing Wetland Assessment: Understanding State Bureaucratic Use and Adoption of Rapid Wetland Assessment Tools. PhD dissertation, Indiana University. Arnold, G., 2014. Policy learning and science policy innovation adoption by street-level bureaucrats. J. Publ. Policy 34 (03), 389–414. Arnold, G., 2015. When cooperative federalism isn’t: how US federal interagency contradictions impede effective wetland management. Publius 45 (2), 244–269. RTI International, NC Department of Environment and Natural Resources, South Carolina Department of Health and Environmental Control, the University of South Carolina, 2011. Assessing Geographically Isolated Wetlands in North and South Carolina—The Southeast Isolated Wetlands Assessment (SEIWA). Final Report. http://www.northinlet.sc.edu/training/media/2011/06142011isolatedwetlands/resources/seiwa_final_report. pdf. Accessed 30 October 2017.
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Smith, R.D., Noble, C.V., Berkowitz, J.F., 2013. Hydrogeomorphic (HGM) approach to assessing wetland functions: guidelines for developing guidebooks (Version 2). ERDC/EL-TR-13-11, Environmental Lab, Engineer Research and Development Center, Vicksburg, MS. Stoddard, J.L., Larsen, D.P., Hawkins, C.P., Johnson, P.K., Norris, R.H., 2006. Setting expectations for the ecological condition of streams: the concept of reference condition. Ecol. Appl. 16 (4), 1267–1276. Tiner, R.W., 2003. Correlating Enhanced National Wetlands Inventory Data With Functions for Watershed Assessments: A Rationale for Northeastern U. S. Wetlands. U.S. Fish and Wildlife Service, National Wetlands Inventory Program, Hadley, MA. US Environmental Protection Agency, US Army Corps of Engineers, 2008. Compensatory mitigation for losses of aquatic resources. Final Rule. April 10, 2008. Fed. Regist. 73 (70), 19594–19705. https://www.epa.gov/sites/production/files/2015-03/documents/2008_04_10_wetlands_ wetlands_mitigation_final_rule_4_10_08.pdf. Accessed 30 October 2017.
Rapid assessment methods (RAMs) are standardized procedures that generate a score, index, or rating for a specified site (individual wetland, stream, watershed, etc.) and/or for its individual ecosystem services (functions, values) or other attributes (e.g., condition, sensitivity), based mainly on ground-level observations and/or by using aerial imagery/GIS as described in this book. The observational nature of RAM procedures contrasts with procedures based mainly on ground-level measurements, such as kilograms of nutrients removed by a wetland or the diversity of the aquatic community in a stream. If a site visit is needed, RAMs commonly require just one such visit lasting less than 1 day. Most RAMs do not require a comprehensive knowledge of plant, insect, or soil taxonomy. This book will help government scientists or others when they are tasked with developing a wetland or stream RAM that is optimized for a particular region, modifying and calibrating an existing RAM from another region so that it is optimal for the user’s region, or calibrating and verifying an existing RAM. The book also will help RAM users make better use of wetland and stream RAMs as they gain a deeper understanding of how RAMs have been developed, tested, and applied. Section and chapter introductions describe many of the lessons we, as editors, have learned from developing RAMs in various regions. Additional insights on how wetland and stream RAMs have been developed and applied can be gained by reading this book’s case histories. RAM development has largely been spurred by a desire for systematic tools to help evaluate impacts to wetlands and streams from development, and to achieve appropriate mitigation based on both the quality and quantity of the resource. RAMs provide key information to those responsible for making decisions about development or conservation of wetlands and streams. RAMs characterize individual stream or wetland sites and may compare them with others. They do so for evaluating project impacts, designing appropriate mitigation, and/or developing strategies that promote conservation. RAMs provide vital information for decision making, but most jurisdictions do not require that RAM results be the sole determinant of a decision about a wetland or stream reach. Many RAMs are localized or regional adaptations of a general template developed previously, such as the hydrogeomorphic (HGM) assessment framework (Smith et al., 2013), the WESP template (Adamus, 2016, Chapter 4.3.2), the Ohio Rapid Assessment Method (Chapter 4.3.8), or the LLWW and NWI-Plus templates for landscape-level assessments (Tiner, 2003; case studies in Section 2.0). RAMs for assessing wetlands are more common than those for streams. Many RAMs are being refined and adapted for use in new geographic areas as well as in response to expanding scientific knowledge, technology, software capabilities, and spatial data availability. In the United States, the impetus for developing wetland and stream RAMs was the Clean Water Act of 1972—the keystone for wetland regulation across the country. Most of the published RAMs have been sponsored by North American states and provinces and are intended to enhance the incorporation of scientific knowledge and existing spatial data into regulatory decisions affecting wetlands and streams. Nearly all RAMs provide one or more scores and/or ratings for an individual site or a larger landscape by using models to process a user’s standardized data inputs. Most often, the models are mathematical formulas that the RAM developer has constructed based partly on published science and partly on correlations thought to exist between various conditions of several indicator variables and an endpoint (wetland or stream functions, values, and/or ecological condition). Some models consist of narrative criteria rather than formulas, and some RAMs do not include explicit models but rather leave it to the user’s judgment to connect the user’s observations of indicator variables to a rating or score intended to represent the endpoint. For only a very few RAMs have the sponsors attempted to validate the presumed correlations between indicators and endpoints by making detailed measurements of the endpoints and comparing those measurements with model outputs to determine if sites are ranked similarly. More often, RAM models are simply verified by comparing the ranking of a series of sites based on model outputs with a ranking based on the independent opinions of local experts on each function 551
552
Summary and Conclusions
or status condition. The repeatability (consistency) of RAM outputs among independent users visiting the same series of sites has apparently been tested in only a few instances. As reflected in the structure of this book, wetland and stream RAMs are commonly developed as either landscape-level or field-level methodologies. Also, based on what they aim to assess, they are often characterized as either functionassessment RAMs (which may also assess the values of those wetland or stream functions as related partly to ecosystem services) or condition-assessment RAMs (which address specific values such as wetland or stream health, quality, status, or integrity). Several RAMs incorporate combinations of these approaches and themes. Condition-assessment RAMs cannot consistently predict all or most functions, and function-assessment RAMs cannot reliably predict the condition of wetlands or streams as it is conventionally defined. Landscape-level wetland RAMs use remotely sensed imagery, existing spatial data layers, and GIS combined with existing knowledge of wetlands to compile input data for models that largely resemble those used in field-level RAMs, and similarly produce scores or ratings believed to reflect wetland or stream functions and/or condition. At present, landscape-level stream RAMs are rare to nonexistent. Landscape-level RAMs are mainly employed when a goal is to assess all wetland or stream sites across a large area (e.g., watershed, river basin, ecoregion) because cost and time requirements to visit and assess all those sites would be prohibitive, even if all sites were accessible for examination. This type of RAM also is an attractive tool for assessing stream segments or wetlands where field-based RAMs have not been developed, or where use of field-level RAMs is difficult or impossible because sites are too remote or hazardous to access, are too large to assess meaningfully during a single 1-day visit, or are inaccessible due to resistance from land owners or managers. Landscape analysis is a first-step, basic assessment derived from interpretation of imagery and maps and existing knowledge of the variety of wetlands that occur across a geographic region. Such analyses can be done any place with suitable imagery where wetlands or streams can be detected and where the relationship between interpreted variables (such as dominant vegetation, hydrology, landscape position, and connectivity) and wetland function or condition is reasonably understood. As mentioned, landscape analyses provide a preliminary assessment as they are limited by source data (aerial imagery and/or GIS data/maps) for a number of reasons, including: (1) not all wetlands and small streams are readily identified on aerial imagery, (2) scale issues (e.g., limitations to the smallest size of wetland or stream that can be identified remotely and displayed on a map), (3) misidentification of an upland as a wetland and inclusions of upland in wetland units and vice versa, (4) misclassification of wetland type, and (5) registration issues (i.e., positional accuracy). Moreover, many important indicator variables (e.g., plant community composition, soil texture, downed wood, microtopography, high water marks on vegetation, concealed outlets, water quality parameters, aquatic macroinvertebrate communities) can be reliably detected only during a site visit and so are lacking in landscape-level RAM models. Consequently, landscape-level RAMs are usually intended for gaining a broad perspective on wetland and stream functions and condition across a watershed or larger geographic area (e.g., analyzing cumulative impacts of development on wetland functions), developing conservation strategies, general planning purposes, and educating the public on expected functions and condition, rather than for making regulatory or management decisions about individual wetlands or project areas. The accuracy of landscape-level assessments can be improved by conducting field investigations to verify wetland and stream classifications and the interpretations of wetland and stream functions or condition. In Alberta, the rank order of a series of wetland sites scored by a field-level RAM was compared with the rank order of the same sites scored independently by a landscape-level RAM, and little concordance was found in the scores or ratings of several functions. An example of the mutually reinforcing effect of landscape-level and field-based analyses is described by work done on geographically isolated wetlands in North and South Carolina (RTI International et al., 2011). The goal of this work was to derive a statistically valid estimate of the extent and condition of isolated wetlands in an eight-county study area in these two states. First, a GIS map was prepared based on a wide variety of GIS layers. Then, a stratified random sample of sites was chosen and field work conducted to determine if the sites were isolated wetlands and, if so, to evaluate their condition using NC WAM (Chapter 4.3.1). Based on this field evaluation, it was determined that the GIS map correctly identified wetlands 69% of the time but only correctly identified isolated wetlands 22% of the time. This high rate of misidentification of isolated wetlands had two main causes: (1) many small ditches were present in the field but not discernable from GIS layers, which then made the sites not isolated, and (2) many sites had been falsely identified as wetlands but were either upland or ponds. However, because the field sample was statistically developed, the authors were able to use data from the GIS map and the NC WAM results to make definitive conclusions about the extent of isolated wetlands in the study area as well as their condition. The combined use of a landscape-level model followed by statistically valid field-level investigations provides a good conceptual model to take advantage of the strength of both these approaches and to minimize the weaknesses of both these approaches. Landscape analysis is often part of field-based RAMs because one cannot see everything from the ground or take time to traverse the entire project area. In addition, landscape-level analysis is often used to address broader watershed or wetland
Summary and Conclusions
553
buffer issues that influence the functioning or condition of a wetland or stream. In general, results from the field-level RAM are preferred over landscape-level assessments because they incorporate many more indicators than landscape-level assessments and involve close-up inspection and verification. However, because ground-level field visits are often physically hazardous, are limited to a 1-day visit, and time-consuming for multiple wetlands, use of remote sensing and GIS analysis can be more accurate for predicting some functions of very large sites or those with poor access. In addition, calibrating a field-based RAM prior to its being made available for public use requires significant time and effort. Of the dozens of field-based RAMs developed and calibrated over the past four decades, only a relatively small number are still used regularly. Why is that? A primary determinant of sustained use is the degree to which sponsors require the use of a particular RAM legally or as official agency policy, or at least unofficially encourage its use (Arnold, 2012, 2014, 2015). Degree of use also depends on the sponsor’s outreach efforts, lack of other RAMs that have been calibrated to the specific region and/or wetland type being assessed, the RAM’s ease of use, the availability of low-cost training for the RAM’s potential users, and the ability of sponsors to invest in continued refinement of the RAM in response to the availability of improved spatial data and imagery, new scientific findings, wider peer review and verification, expanded repeatability testing, and validation by comparison with more intensive measures of function and condition. RAM sponsors must decide how frequently to release revisions and updates so as to avoid creating a “moving target” that can frustrate users if a site’s rating changes over time as a result of a sponsor’s well-intended revisions of the RAM. As agency budgets and availability of qualified staff decrease, RAM developers and their sponsors will likely find themselves pressured to make their RAM shorter, faster, and simpler. Can this be done while still achieving sufficient accuracy and achieving a desired application outcome such as avoidance or minimization of impacts? Often the requests for RAM streamlining originate from the regulated community out of a desire to minimize project costs by shortening the time required to do an assessment. In some instances limited streamlining is possible without compromising the scientific integrity, sensitivity, repeatability, or accuracy of the RAM, but regulatory agencies with input from scientists must decide the extent to which this is possible. While most RAMs have been developed by regulatory or natural resource agencies for specific uses in specific areas, the scientific assumptions their models employ to assess what seems to be the same thing (e.g., a particular function) sometimes differ among RAMs. For example, some wetland RAMs assign highest scores for water storage function to river-associated sites whereas others assign the highest to depressional (closed basin, isolated) wetlands. This could be due to differing definitions of the water storage function (especially whether a site’s area is included in the score calculation), a confusion of functions with values, a misunderstanding of physics and wetland science, or regional differences in how wetlands function. Better-documented rationales and broader consensus need to be sought concerning each of the hundreds of correlations assumed between various functions and their indicator variables. Support is warranted for the development or regional adaptation of templates for assessing wetlands in states, provinces, and nations that currently lack wetland or stream RAMs. Even more pressing is the need for developing or adapting RAMs applicable to the functions of stream and riparian segments outside the Mid-Atlantic and Pacific Northwest portions of North America, and RAMs for assessing functions of aquatic habitats other than wetlands and streams. For many RAMs, more effort should be made to incorporate knowledge of indigenous peoples when identifying and modeling suitable indicators of functions, values, and site condition. The potential for specifying the use of emerging technologies (e.g., drones, sensors, software, and modeling techniques) in RAMs should be explored fully with a goal of producing more consistent and accurate results at less cost. Better scientific information is needed with regard to how to quantify, quickly and accurately, the short and long term as well as cumulative effects of various types of stressors on site functions using only onevisit field observations and conventionally available imagery. As described in Section 3.0, most wetland and stream RAMs focus either on assessing site functions or assessing site health (condition or quality), and these two are not synonymous. Additional research is warranted to determine under what conditions (which functions, regions, wetland types, and metrics) the RAM-generated predictions of wetland health match RAM-generated predictions of each wetland function, and vice versa. This may increase confidence that the results of using one or the other type of RAM adequately represent what is assessed by the other. The term “tested” is widely used by RAM authors hoping to inspire confidence among users, yet a review of existing RAMs suggests it can mean anything from casual examination of RAM results from a few wetlands by its author to correlation with results from other RAMs to rigorous comparison of results with direct, long-term measurements of function across a large enough series of sites verified to statistically encompass the variation of conditions present in a region. A need exists for measuring the repeatability of many RAMs (and, especially, using the resulting confidence intervals around the scores to ensure the correct use of the scores for interpreting functional “lift” or impact over time). As budgets and time allow, more effort should especially be devoted to verifying and validating parts of the many RAMs currently in use as well as measuring the repeatability of RAM scores and ratings among independent users assessing the same sites.
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Summary and Conclusions
Guidance is needed from statisticians for appropriate ways of determining the adequate number and type of sites for field calibrating a RAM, and for evaluating under what conditions a RAM can be validly extended into regions where it was not field calibrated. With such guidance, the adequacy of the number of sites where existing RAMs have been calibrated should be examined to determine if more calibration sites need to be added in each RAM’s focal region. Adding more calibration sites within any region will allow existing RAMs to be calibrated more precisely by allowing the RAM’s scores to be normalized by specific wetland types or settings. Thus, a rating of “high,” for example, could represent a high level of a particular function or condition relative to other sites of that wetland type, or relative only to (say) other wetlands in an urban setting rather than relative to all wetlands within the RAM’s region regardless of type or setting, which is often the case with existing RAMs. What then is the future of RAMs? Based on the work that the authors have done and the chapters included in this book, we foresee several trends and make related recommendations: (1) The work to validate RAMs will continue and, we recommend, should be accelerated. We recommend that government agencies (and other funding sources) recognize the importance of this effort and adequately fund this work. In the case of the United States, we acknowledge the importance of the U.S. EPA’s Wetland Program Development Grant Program and suggest that it explicitly list validation of both function and condition RAMs as a funding priority. (2) RAMs for streams and other ecosystems will be more common in the future. In the United States, this seems like a logical result of the joint mitigation rule of the U.S. Army Corps of Engineers and U.S. EPA (US Environmental Protection Agency and US Army Corps of Engineers, 2008). We suggest that these agencies prioritize the development and testing of regional RAMs. In addition, the administrators of these agencies should prioritize implementation of the joint mitigation rule by their staffs. (3) Despite a growing need, RAMs in developing countries will largely continue to be based on professional judgment due to scarcity of scientific data, spatial data, and skilled professionals. Improving wetland inventories for application of landscape-level approaches should be the first step in wetland assessment for large geographic regions, providing the broad overview of wetlands and their functions for strategic conservation planning. We suggest that international organizations (such as the United Nations and the Ramsar Convention) make development and testing of RAMs in developing countries a high priority. A significant role may be played by regional organizations such as the Organization of American States for RAM development in Latin America because RAMs are apparently very sparse in this region. As a parallel effort, we suggest that scientific organizations (such as the Society of Wetland Scientists and the Society of Freshwater Scientists) make outreach and assistance to these countries to develop and test RAMs a priority. (4) RAMs that require “relatively undisturbed” reference sites to calibrate their scores will need to consider other ways to establish reference condition in highly developed landscapes, such as many in Europe, India, and China. Given contemporary environmental conditions, the most practical reference sites may just have to be those that seem to be subjected to the fewest current stressors, rather than pristine sites (Stoddard et al., 2006). (5) The outputs of RAMs will be more explicitly incorporated into regulatory programs as the value of the results of these RAMs becomes more apparent to the regulated community, agencies, and the general public. To accelerate this process in the United States, we suggest that organizations such as the Association of State Wetland Managers and agencies such as the U.S. EPA and the U.S. Army Corps of Engineers expand their current efforts and funding for explaining the utility of wetland and stream RAMs to the general public. More funding for updating and enhancing the U.S. Fish and Wildlife Service’s National Wetlands Inventory (NWI) will make NWI data more useful for landscape-level wetland function assessments across the country.
REFERENCES Adamus, P.R., 2016. Manual for the Wetland Ecosystem Services Protocol (WESP). Version 1.3. Internet: people.oregonstate.edu/adamusp/WESP. Arnold, G., 2012. Assessing Wetland Assessment: Understanding State Bureaucratic Use and Adoption of Rapid Wetland Assessment Tools. PhD dissertation, Indiana University. Arnold, G., 2014. Policy learning and science policy innovation adoption by street-level bureaucrats. J. Publ. Policy 34 (03), 389–414. Arnold, G., 2015. When cooperative federalism isn’t: how US federal interagency contradictions impede effective wetland management. Publius 45 (2), 244–269. RTI International, NC Department of Environment and Natural Resources, South Carolina Department of Health and Environmental Control, the University of South Carolina, 2011. Assessing Geographically Isolated Wetlands in North and South Carolina—The Southeast Isolated Wetlands Assessment (SEIWA). Final Report. http://www.northinlet.sc.edu/training/media/2011/06142011isolatedwetlands/resources/seiwa_final_report. pdf. Accessed 30 October 2017.
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Smith, R.D., Noble, C.V., Berkowitz, J.F., 2013. Hydrogeomorphic (HGM) approach to assessing wetland functions: guidelines for developing guidebooks (Version 2). ERDC/EL-TR-13-11, Environmental Lab, Engineer Research and Development Center, Vicksburg, MS. Stoddard, J.L., Larsen, D.P., Hawkins, C.P., Johnson, P.K., Norris, R.H., 2006. Setting expectations for the ecological condition of streams: the concept of reference condition. Ecol. Appl. 16 (4), 1267–1276. Tiner, R.W., 2003. Correlating Enhanced National Wetlands Inventory Data With Functions for Watershed Assessments: A Rationale for Northeastern U. S. Wetlands. U.S. Fish and Wildlife Service, National Wetlands Inventory Program, Hadley, MA. US Environmental Protection Agency, US Army Corps of Engineers, 2008. Compensatory mitigation for losses of aquatic resources. Final Rule. April 10, 2008. Fed. Regist. 73 (70), 19594–19705. https://www.epa.gov/sites/production/files/2015-03/documents/2008_04_10_wetlands_ wetlands_mitigation_final_rule_4_10_08.pdf. Accessed 30 October 2017.