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Application of the landscape development intensity (LDI) index in wetland mitigation banking
Ecological Modelling 271 (2014) 83–89
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Ecological Modelling journal homepage: www.elsevier.com/locate/ecolmodel
Application of the landscape development intensity (LDI) index in wetland mitigation banking Kelly Chinners Reiss a,∗ , Erica Hernandez b,1 , Mark T. Brown a a b
Howard T. Odum Center for Wetlands, 100 Phelps Lab, Museum Road, University of Florida, Gainesville, FL 32611-6350, United States Department of Environmental Protection, Kissimmee Prairie Preserve, 33104 NW 192nd Avenue, Okeechobee, FL 34972, United States
a r t i c l e
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Article history: Available online 31 May 2013 Keywords: Florida Human disturbance gradient Landscape development intensity Wetland mitigation bank
a b s t r a c t The landscape development intensity (LDI) index, which can be used as a human disturbance gradient, is an effective tool in assessing location of wetland mitigation banks where large tracts of land are managed to protect wetland function by offsetting wetland losses from off-site land development. As part of a larger study to determine the effectiveness of mitigation banking in Florida, this article focuses on characterizing the landscape intensity of wetland mitigation banks in the state. Two scales of the LDI index were calculated: wetland assessment area scale LDI index (n = 58), which characterizes the landscape surrounding a small parcel of land within a mitigation bank boundary, and mitigation bank scale LDI index (n = 26), which characterizes the lands surrounding the entire boundary of the wetland mitigation bank. Approximately two-thirds of the wetland assessment areas (n = 38) had LDI index scores less than 2.0 (where 0.0 represents no human development), with a mean LDI index score of 3.2 ( = 4.9). LDI index scores were calculated such that all lands within the 100 m zone surrounding a wetland assessment area designated as restoration, enhancement, creation, or preservation were assigned LDI index scores reflecting natural lands. In this application, the LDI index score was considered a tool to predict potential wetland condition based on successful restoration. Bank scale LDI index scores, based on land use within the 100 m zone surrounding the entire mitigation bank, were generally higher than assessment area scores, with a mean bank scale LDI index score of 7.8 ( = 5.4) and a median of 6.5. At the wetland scale, evaluation of landscape condition using the LDI index presented a quantitative analysis tool to determine potential ecological lift (i.e. expected gain in ecological condition) with successful wetland restoration practices within the mitigation bank. Whereas at the bank scale, LDI index calculations suggested the expected impacts from human land use activities outside of the control of the bank property. We propose using the LDI index to facilitate calculation of wetland mitigation potential and credit awards for mitigation banks, and that regardless of the tool used consideration of potential wetland functional lift should incorporate a landscape perspective. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Historically, wetlands have been considered areas to conquer, with humans draining or filling wet areas to create lands more suitable to human land use activities; however, overtime, we have recognized the ecosystem services provided by wetland systems such as air and water purification, wildlife habitat, and storage of floodwaters, and efforts have been made to protect wetland systems. One mechanism to compensate for wetland loss that has
∗ Corresponding author. Tel.: +1 352 392 2424. E-mail addresses: kcr@ufl.edu (K.C. Reiss), echernandez@ufl.edu (E. Hernandez), mtb@ufl.edu (M.T. Brown). 1 Current address: Howard T. Odum Center for Wetlands, 100 Phelps Lab, Museum Road, University of Florida, Gainesville, FL 32611-6350, United States. Tel.: +1 352 392 2424. 0304-3800/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ecolmodel.2013.04.017
gained attention at the national scale over the past two decades is wetland mitigation banking. A wetland mitigation bank consists of a land area protected from human development activities and often actively managed for enhanced ecological function or ecological restoration. Because humans are known to damage and degrade natural environments, install impervious surfaces, and remove native habitat cover, state and federal regulations in the United States require avoiding, minimizing, and mitigating impacts to wetland ecosystems. In wetland mitigation banking systems, banks with restored, enhanced, created, or preserved land are established and awarded credits, which are purchased by land developers to compensate for impacts to off-site wetlands. Credits are determined based on expected lift, or a best professional judgment of the expected with-mitigation condition (an anticipation of the future condition) compared to the current condition. While there are requirements for establishing wetland mitigation banks ranging from funding, easements, service areas, and
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Fig. 1. Aggregate systems diagram of a wetland embedded within a developed landscape. The systems boundary is a 100-m buffer around the delineated wetland edge, which could be used for the landscape development intensity (LDI) index calculation.
land management, this paper addresses one of the nine criteria identified for establishing mitigation banks as identified in Florida Statutes Section 373.4136, F.S. requiring that banks be located adjacent to lands that will not adversely affect the viability of the wetland mitigation bank. Acknowledging the influence of the adjacent lands and understanding the landscape location of a wetland mitigation bank could inform credit award and expected long-term ecological condition. In order to quantify the landscape condition surrounding wetland mitigation banks, we propose using the landscape development intensity (LDI) index, which can be used as a human disturbance gradient. Owing to the unique position of wetlands in the landscape, receiving and filtering airborne and aquatic pollutants from nonpoint and direct point source inflows, the LDI index represents a human disturbance gradient for wetland systems based on nonrenewable energy use (e.g., fertilizer, fuel and electricity) in the surrounding landscape. An aggregate systems model of the inflows to a wetland embedded in a developed landscape is presented in Fig. 1. The LDI index reflects local human activity, combining the effects from air and water pollutants, physical damage, and changes in the suite of environmental conditions (e.g., nutrient load, water levels) on the structure, process, and function of ecosystems. The major advantages of a LDI index tool include the ease and speed of calculation, the widespread availability of digital data, the repeatable and objective evaluation of wetlands, and the capability to perform the assessment from the office, avoiding seasonal and other field constraints (e.g. time commitment, labor, costs, travel and field conditions). The LDI index calculates the amount of nonrenewable energy use weighted by area of land use within a geographic information systems (GIS) framework (Brown and Vivas, 2005). Natural, undeveloped land use/land cover (LU/LC) classes have a LDI index value of zero and low intensity recreational areas, unimproved native pastures, and open spaces have an LDI index around one. Agrarian land uses have slightly higher LDI index values ranging from 3 to 6
for managed pasture and 7–14 for other agricultural land uses (e.g. citrus, row crops, dairy production). Increasing energy use in low intensity urban LU/LC classes such as two-lane roads and singlefamily housing have an LDI index ranging from 20 to 27. The highest intensity land uses have LDI index values greater than 30 including institutional, commercial, industrial, multi-family housing, and commercial districts. The earlier LDI index presented by Brown and Vivas (2005) had a scale from 1 to 10; then a modified equation was developed by Vivas (2007) and later presented by Reiss et al. (2010) with a scale from 0 to 42, with 42 representing the highest land use development category in Florida, central business district average four stories. Some of the earliest work developing the LDI index concept related water quality in the St. Marks watershed in Florida to human development intensity (Brown, 1980; Brown et al., 1998; Parker, 1998). The LDI index has been used in several studies of Florida wetlands (e.g., Reiss, 2006; Lane and Brown, 2007; Nestlerode et al., 2009; Reiss et al., 2010) and also in other states (e.g., Minnesota – Bourdaghs et al., 2006; Ohio – Mack, 2006; Arkansas – Vivas and Brown, 2006) and more recently outside of the lower 48 states (e.g. Taiwan – Chen and Lin, 2011; Hawaii – Margriter, 2011; St. Croix – Oliver et al., 2011). As part of a larger study to determine the effectiveness of mitigation banking in Florida (Reiss et al., 2007), the specific objective of this paper is to calculate the LDI index for existing wetland mitigation banks to inform the assessment of wetland condition. Land areas within the wetland mitigation banks undergoing restoration, enhancement, creation, or preservation activities were assigned the development intensity of natural land. Clearly mitigation activities require nonrenewable energy use (e.g., earth moving activities such as ditch filling, exotic plant removal or herbicide treatment); however, the calculated LDI index values for the wetland assessment areas were considered “potential” LDI index, which would be attained at a restored, self-sustaining community given ecological project success. Thus, the calculated LDI index value reflects
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Fig. 2. Location of 29 wetland mitigation banks included in this study. Wetland region boundaries according to Lane (2000).
the lowest potential LDI index score and in turn the highest potential ecological condition, once mitigation activities are complete. In this way, the LDI index can inform best professional judgments on future wetland condition, an important step in awarding credit in wetland mitigation banking. 2. Materials and methods In total 29, wetland mitigation banks were included in this study located throughout the four Florida wetland regions (Lane, 2000). A single wetland mitigation bank was located in the panhandle wetland region. Just over half of the study mitigation banks (n = 15) were within the central wetland region, with 10 in the south wetland region, and three in the north wetland region (Fig. 2). This distribution reflects the higher population density of south and central Florida and the more rapid development of lands in those regions based on market demand for wetland credits. Detailed information on mitigation banks in Florida including acreage, credits awarded, credits released, and contact information, can be accessed on the web (http://www.dep. state.fl.us/water/wetlands/mitigation/mitigation banking.htm). Within each bank, smaller wetland assessment areas were identified for evaluation. While some banks were relatively small in land area and homogeneous in wetland community type, many covered large land areas and contained a variety of wetland community types. The number of wetland assessment areas selected depended on a combination of site-specific conditions such as homogeneity of wetland community types, mitigation activities completed to date, and progress toward permit success criteria; area of wetland; type of mitigation (e.g., restoration, creation, enhancement, or preservation); and general site conditions. The specific area for each wetland assessment area was determined at the time of field visits, which occurred within the context of the larger project by Reiss et al. (2007) and not specifically for this GIS based analysis. Selection of wetland assessment areas was based on site
conditions and physical access as determined by the land manager and/or mitigation bank owner. In general, wetland assessment areas represented an example of current wetland condition and were not expected to have a 1:1 relationship to the condition of the entire wetland mitigation bank. That is, wetland assessment areas provide insight to smaller land areas but are not meant as a surrogate to represent condition within the entire mitigation bank. Another important note perhaps of interest to the reader, many wetland mitigation banks in Florida include a mosaic of upland and wetland habitats, necessitating delineation of smaller wetland assessment areas within a bank to inform understanding of wetland condition. Two scales of the LDI index were calculated: wetland (assessment area) scale LDI index (n = 58) and (mitigation) bank scale LDI index (n = 26). To calculate the wetland scale LDI index, a 100 m zone was delineated around the edge of each wetland assessment area and LU/LC classes within the zone were identified based on 2004 digital orthographic quarter quads and field notes for current surrounding land use. To calculate the bank scale LDI index, a 100 m zone was identified around the bank boundary and LU/LC classes within the zone were identified using 2000 land use cover maps, available from the Florida Geographic Data Library (http://www.fgdl.org/). Only 26 of 29 bank scale LDI calculations were completed as the mitigation bank outline was not available for one bank; two phases of one bank were combined into one bank scale LDI index; and year 2000 land use was not available for the bank in the panhandle. 2.1. Calculation The LDI index provides a quantitative measure of nonrenewable energy use per unit area of landscape surrounding a specific point or polygon. Non-renewable resources are defined as resources that are not renewed on a time scale relevant to the average human life span. Since all resources have differing energy quality (Cleveland,
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Table 1 Surrounding land use classification for wetland mitigation bank sites based on broad landscape development intensity (LDI) index categories. Note these are not the actual dominant land uses surrounding a particular site, but a representation of the approximate land use based on the LDI index value. General land use/land cover category
Natural lands Minimal disturbance Low intensity disturbance – open space, native pasture Managed pasturelands Agriculture – citrus, row crops, dairy Mixed agricultural and/or low intensity urban
LDI index range
0.0 >0.0 to ≤0.5 >0.5 to ≤3.0 >3.0 to ≤6.0 >6.0 to ≤14.0 >14.0 to ≤18.5
2012) or ability to do work, calculation of the LDI index includes quantifying the nonrenewable energy in units of the same energy form, customarily solar energy. Odum (1996) used the term emergy, the expression of different forms of energy converted to a single form of energy, in early innovative work in environmental accounting. Emergy is the amount of available energy of one form required to produce a flow of material, energy, or information (Odum, 1996). Emergy can be coupled to flows of resources and to storages or stocks. When resource flows (i.e. energy per time, or power) are expressed in their emergy equivalents the resulting quantity is termed empower, or emergy per time. When empower is computed on an areal basis (i.e. sej time−1 area−1 ) the resulting quantity is termed areal empower intensity (AEI). The LDI index employs the AEI of land uses, converting them to a non-dimensional index (Brown and Reiss, 2010). The LDI index is calculated using ESRI® ArcMapTM 10.0 and a spreadsheet software program (e.g. Microsoft Office 2010 Excel software). In general, the method involves identifying the sampling point of interest, digitizing a polygon around the point of interest, clipping the LU/LC layer within the boundary of the polygon, summing the area-weighted AEI values of each LU/LC class within the polygon, and computing the LDI index score. In order to calculate the LDI index several resources are needed including digital imagery (e.g. digital orthophoto quarter quad (DOQQ) imagery, high resolution digital imagery), a land use/land cover (LU/LC) shapefile, and a look-up table for the AEI value for each LU/LC class. The LDI index value is calculated as: LDI = 10 × log10
AEI
Total
AEIRef
(1)
where LDI, LDI index for a given polygon, AEITotal , total areal empower intensity (AEI) within the polygon (including the background environment), and AEIRef , renewable AEI of the background environment within the polygon. The total areal empower intensity (AEITotal ) is calculated as follows: AEITotal = AEIRef +
(%LUi × AEIi )
(2)
where LUi , the fraction of the total delineated zone in land use i, and AEIi , the nonrenewable areal empower intensity for land use i. 2.2. Non-renewable areal empower intensity Reiss et al. (2010) present the non-renewable AEI values for LDI index categories for Florida. Some additional AEI values have been calculated for different states, but most states currently lack AEI values for statewide LU/LC classes. While many users may rush to calculate the LDI index with any available AEI values, we offer a word of caution. The first step in developing a reliable LDI index tool for a state is to begin with a complete emergy evaluation of the state and all major human land use activities (e.g. specific farming crops, cattle stocking densities, residential development density, commercial land uses). While there may be some overlap
Wetland Scale
Bank Scale
Count (n)
Percent (%)
Count (n)
Percent (%)
16 15 10 3 12 2
28 26 17 5 21 3
1 2 2 6 11 4
4 8 8 23 42 15
in land use intensity across regions, these activities have variable inputs and requirements depending on local factors and the natural background environment. The AEIRef used for Florida’s background environment is 1.99 E15 sej ha−1 yr−1 . 3. Results Mean bank area was 848 ha ( = 894.5 ha) with a range from 27 ha at Graham Swamp to 3653 ha at Everglades Mitigation Bank/Phase II. The size of the wetland assessment areas ranged from 0.2 ha at Lake Monroe to 282.2 ha at Loxahatchee, with a mean wetland assessment area of 20.3 ha ( = 48.3 ha). The mean wetland assessment area was 15.8% ( = 30.8%) of the total mitigation bank area, with a range spanning from 0.1% at Loblolly Mitigation Bank and TM-Econ to 100% at Bear Point and Graham Swamp. Less than 1% of the bank area was included in the wetland assessment areas at 13 banks, while over 50% of the bank area was included in the wetland assessment areas at four banks. The mean wetland scale LDI index score was 3.2 ( = 4.9) with a median of 0.3 and a range from 0.0 to 16.7. The distribution of LDI index scores was non-normal (Shapiro–Wilk W = 0.7029, p < 0.01), with 16 assessment areas with wetland scale LDI index scores of 0.0 (Table 1). An additional 25 wetland scale LDI index scores were less than 3.0, so that over 70% of wetland scale LDI index calculations were below 3.0, which roughly suggests low levels of human disturbance in the immediately adjacent lands at the wetland scale. Approximately 5% of wetland scale LDI index scores were between 3.0 and 6.0, and the remaining 25% were greater than 6.0, representing impacts from agricultural to low intensity urban land uses. Bank scale LDI index scores were higher, with a mean bank scale LDI index score of 7.8 ( = 5.4), a median of 6.5, and a range from 0.0 to 18.2. One mitigation bank had a bank scale LDI index score of 0.0 (Table 1); this mitigation bank is an island situated in an aquatic preserve. Six banks had a range of bank scale LDI index scores from 3.0 to 6.0. In contrast to the wetland scale, the majority of the bank scale LDI index scores were greater that 6.0 (57%). A weak correlation was found between wetland scale and bank scale LDI index scores (r = 0.27, p < 0.05). Fig. 3 shows several significant discrepancies between wetland scale and bank scale LDI index scores. In particular, some wetland assessment areas have significantly higher wetland scale LDI index scores due to proximity to interior paved roads or built structure (e.g. offices, recreational facilities). The impervious surfaces are considered permanent structures within the footprint of the wetland mitigation area. On the other end of the scale, some wetland scale calculations had smaller LDI index scores than the bank scale LDI index, often because of the interior location of the wetland assessment areas, buffered by the mitigation lands from the impacts of roads or other human land uses occurring along the bank property boundaries. 4. Discussion The application of the LDI index to mitigation sites presents many challenges. Primarily, there is concern over how to assign AEI
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Fig. 3. Wetland scale (light blue bars) and bank scale (dark green bars) landscape development intensity (LDI) index scores. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
values to lands that in the past have been used for human activities but currently are being restored or enhanced to a more natural condition. As such, wetland scale LDI index calculations used in this study have been considered “potential” LDI, suggesting the wetland condition attainable based on successful mitigation activities. Further, differences between wetland and bank scale LDI scores raise the question as to the most important or relevant scale for considering impacts from human disturbance to wetland systems. 4.1. Sustainability and landscape position Much of the land area, especially in proximity to wetlands, has been cross ditched and drained since human settlement began in Florida. Understanding the limitations of landscape location in with-mitigation scenarios is necessary when assessing lift (i.e. improvement in ecological condition given completion of mitigation activities) in mitigation bank credit awards. A bank surrounded by developed land uses or located in areas that are expected to support human development activities in the future should not anticipate full credit award for with-mitigation scenarios, as the anticipated ecological function would vary from the reference standard (i.e. wetlands not located near human development activities). Consideration of potential wetland functional lift should incorporate a landscape perspective, which could include application of available tools such as variable scales of the LDI index, reflecting local and broad scale landscape support for wetland condition. Within a highly developed country, finding a significantly large tract of land that wholly insulates mitigation wetlands from human impacts may be impossible. For example, Forman (2000) suggests that approximately 20% of the land area within the United States has experienced direct ecological effects from the public road system alone. This 20% figure, which does not include secondary or indirect impacts, is expected to rise in the future, suggesting that roadways will continue to act as a major influence on community structure and wildlife habitat suitability within wetland mitigation banks. For example, mitigation banks adjacent to or bisected by highways is a concern as highways act as significant barriers for wildlife dispersal. In a study on the mortality of amphibians, reptiles, and other wildlife along a two-lane paved causeway, Ashley and Robinson (1996) found over 32.000 individuals died in two two-year periods along the road. In another study from Boston, Massachusetts, researchers found the impacts of a major four-lane
road extended at least 100 m and perhaps more than 1 km for some effects (Forman and Deblinger, 2000). Further examples of the significant impacts that roads play in disturbing wildlife include the mortality of herpetofauna along US-27 in Lake Jackson, Florida (Aresco, 2005), which are a commonly overlooked though major biotic component of freshwater wetlands (Gibbons, 2003). In addition, there may be impacts on the largest protected species. In one example, Maehr (1988) found that female Florida panthers (Felis concolor coryi) rarely cross major roads or use underpasses, so their habitat is still essentially fragmented by roads even where underpasses have been constructed (Maehr, 1988, as cited by the Florida Fish and Wildlife Conservation Commission, n.d.). These studies suggest that roadways and conservation areas should be well separated, and yet many mitigation banks border busy roadways or busy roads bisect them. A further concern regarding landscape support is the presence of tall transmission towers on or near bank properties that may occur within avian flight paths. Species attracted to mitigation banks may be endangered by neighboring structures. For example, in a study on bird mortality in central Florida, Taylor (1973) found hundreds of black-throated blue and Cape May warblers killed in a six-week period in September and October at the WDBO-WFTV TV Tower, in the autumns of 1969–1972. In a more recent study by Crawford and Engstrom (2001), 44,007 individuals of 186 species were collected at the WCTV television tower in north Florida, and over 94% of the total number of individuals was neo-tropical migrants. The study spanned a 29-year period, one of the longest of its kind. They found that towers approximately 94 m or lower might not pose as great a threat to avian mortality as caused by towers 200 m or greater. While many of the supporting studies are several years to decades old, the rapid increase in transmission towers across the urban and rural landscape suggests such findings are even more significant today. For example, in a recent meta-analysis of bird mortality from communication towers, Longcore et al. (2012) reported approximately 4500 towers greater than or equal to 150 m in height and over 70,000 towers greater than or equal to 60 m in height across the continental US and Canada. A recent study on the demographics of Florida mitigation banks suggested that the location of banks in more rural areas redistributes wetland resources and the associated ecosystem services away from urban areas and thus removes some of the services afforded by these systems (Ruhl and Salzman, 2006). Locating banks within developed urban areas may improve the
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distribution of certain ecosystem services across the landscape (e.g., flood attenuation), but such a location is not expected to reproduce the suite of ecosystem services attained in a more natural landscape setting. A national study by Brown and Lant (1999) found that the spatial location of banks was often in downstream or coastal locations, so that replacement may not be providing equal function compared to wetlands lost in the upper watershed. While the contrast between impacted site location and mitigation bank location is not a focus of this study, it does warrant further investigation. In one recent study Gebo and Brooks (2012) describe most natural wetlands in Pennsylvania as riparian systems with most contributing to headwater streams; the authors point to findings by Brody et al. (2008) and Kettlewell et al. (2008) that most mitigation wetlands are isolated depressions. In this case, because wetland mitigation allows trading riparian wetlands for isolated depressions, we expect a shift in the functions (e.g. water purification, wildlife habitat, water storage, flood attenuation) expressed in the mitigated wetland types compared to those being lost in development. For example, significant loss in wetland function from relocating mitigation wetlands toward the bottom of a watershed may manifest in loss of flood control (Ogawa and Male, 1986 as cited by Brown and Lant, 1999) and change in water quality benefits (Peterjohn and Correll, 1984), which should be taken into consideration. Wetland mitigation must be considered a trade-off between temporal and spatial ecosystem function, and we must maintain realistic expectations of attainable function in the calculated withmitigation scenario in light of impacts from roadways, transmission towers, wildlife behavior, and unmentioned but more obvious impacts like pesticide over-spray and emissions from fossil fuel combustion. That is, when a bank is adjacent to developed lands, the location and landscape functional component should never be awarded full credit for landscape location. 4.2. Ecological integrity Before compensatory wetland mitigation is considered, state and federal regulations propose that first wetland impacts are avoided and second that unavoidable wetland impacts are minimized. Remaining wetland impacts are then mitigated with the intention of replacing lost wetland function and achieving the mandated no net loss of wetland function. The impetus for wetland protection comes from protecting the physical, biological, and chemical integrity of our Nations waters, particularly in matters of human health and economic concerns (e.g., coastal fisheries, navigation). Defining ecological integrity of a delineated wetland or water body can be done in a number of different ways, each reaching a somewhat different conclusion depending on what was measured and how it was quantified. There is currently no single scientifically agreed upon best method to assess the ecological integrity of an ecosystem. However, developing a repeatable and objective measure of ecological integrity or a surrogate for integrity (e.g. condition) that is easy to implement and unambiguous would be ideal. Wetland assessment areas in this study located in mitigation banks that had achieved final permit success criteria and had all potential credits released did not receive the highest possible scores using field assessment methods (Reiss et al., 2007), suggesting full wetland function had not been attained at the time of credit release or thereafter. Some banks near busy highways, receiving polluted water (e.g., receiving water from a canal that receives urban stormwater runoff), or adjacent to high intensity human development activities (e.g., high-density single family residential), were assumed in the permit to have the potential to provide full wetland function (Reiss et al., 2009). However, the landscape of Florida has become more urban and Florida has one of the highest
rates of conversion of rural to urban land use (Reynolds, 2001). Realistic with-mitigation scenarios should be of primary importance for determining potential credit awards for a mitigation bank. When permits assume that the with-mitigation scenario attains full wetland function and success criteria are established based on assumed attainment of full wetland function, any wetland (or upland) community in a bank that falls short of full wetland function represents a potential net loss of wetland function, which is of critical national concern. In a recent meta-analysis of global wetland restoration projects MorenoMateos et al. (2012) conclude that current restoration projects have limited performance (e.g. structure, biochemical processing), and if we proceed with current restoration we will experience an increasing global loss of structure and function of wetland ecosystems. 4.3. Credit potential Florida rules recognize that not all mitigation areas are expected to attain “reference condition.” Chapter 62.312.350, F.A.C. states that “it is not the intent of the Department to require that the mitigation area exactly duplicate or replicate the reference water.” Thus, an important concept in evaluating mitigation success is an understanding of how a mitigation site is assessed for credit. In Florida, a mitigation credit is defined as a “standard unit of measure which represents the increase in ecological value resulting from restoration, enhancement, preservation, or creation activities” (Section 373.403(20), F.S. and Chapter 62-342.200(5), F.A.C.). Typically, the mitigation area is divided into polygons or assessment areas of similar condition, treatment, community type, and anticipated results. Each area is assessed for anticipated functional lift between the current or predicted without bank condition compared to the anticipated with bank condition (Story et al., 1998). In reviewing permit files and attachments for Florida mitigation banks, it was clear that the with-mitigation bank scenario was scored high in anticipation that full function would return to a site once mitigation activities were completed (Reiss et al., 2009). This was true even in cases where the surrounding landscape would have an impact on water quality or quantity or where wildlife support or movement was significantly curtailed. It often seemed that the assessment was focused only on the anticipated capacity to support vegetation rather than the full suite of integrated wetland functions of the community. This practice leads to an over-estimation of ecological lift and elevated mitigation credit award. 5. Conclusion Return of full wetland function may be an impossible goal given current and future human development activities across the landscape. A more realistic outlook on wetland mitigation outcomes would probably reduce the amount of potential credits allocated for a particular site. Some may argue that this would reduce the economic incentive for mitigation banking; however, economic evaluation is beyond the scope of this study and existing wetland regulatory rules. Many wetland mitigation banks were limited by their position in the landscape as identified quantitatively by high bank scale LDI index values. The result of being located in a developed landscape may manifest as managed hydrologic regimes and altered benefits to downstream systems, water quality concerns, or other barriers to attaining full function. Using a quantitative, objective index of landscape condition like the LDI index to assess mitigation credit potential for the landscape location setting could inform the calculation of with-mitigation credit awards.
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Acknowledgments Extensive cooperation between state partners, private consultants, and private landowners was needed to carry out the larger study on Florida mitigation. In particular, staff at the Florida Department of Environmental Protection contributed to this research effort. This project was funded in part by a United States Environmental Protection Agency (USEPA) Region IV grant to the Division of Water Resource Management of the Florida Department of Environmental Protection. References Aresco, M.J., 2005. Mitigation measures to reduce highway mortality of turtles and other herpetofauna at a north Florida lake. Journal of Wildlife Management 69 (2), 549–560. Ashley, E.P., Robinson, J.T., 1996. Road mortality of amphibians, reptiles and other wildlife on the Long Point Causeway, Lake Erie, Ontario. Canadian Field Naturalist 110 (3), 403–412. Bourdaghs, M., Campbell, D., Genet, J., Gernes, M., Brandt-Williams, S., 2006. Development of the Landscape Development Index as a Wetland Condition Assessment Tool. Final status report under EPA assistance AW-96569201-0. US Environmental Protection Agency, Washington, DC. Brody, S.D., Davis, S.E., Highfield, W.E., Bernhardt, S.P., 2008. A spatial temporal analysis of Section 404 wetland permitting in Texas and Florida: thirteen years of impact along the coast. Wetlands 28, 107–116. Brown, M.T., 1980. Energy basis for hierarchies in urban and regional landscapes. University of Florida, Florida (Doctoral dissertation). Brown, P.H., Lant, C.L., 1999. The effect of wetland mitigation banking on the achievement of no-net-loss. Environmental Management 23 (3), 333–345. Brown, M.T., Reiss, K.C., 2010. Landscape development intensity and pollutant emergy/empower density indices as indicators of ecosystem health. In: Jørgensen, S., Xu, L., Costanza, R. (Eds.), Handbook of Ecological Indicators for Assessment of Ecosystem Health. , 2nd ed. CRC Press, Florida. Brown, M.T., Vivas, M.B., 2005. A landscape development intensity index. Environmental Monitoring and Assessment 101, 289–309. Brown, M.T., Parker, N., Foley, A., 1998. Spatial modeling of landscape development intensity and water quality in the St Marks River watershed. Report to Florida Department of Environmental Protection under contract #GW138. University of Florida, Florida. Chen, T., Lin, H., 2011. Application of a landscape development intensity index for assessing wetlands in Taiwan. Wetlands 31, 745–756, doi:10.1007/s13157-0110191-6. Cleveland, C., 2012. Energy quality. In: Cleveland, C.J., Budikova, D. (Eds.), Encyclopedia of the Earth. Environmental Information Coalition, National Council for Science and the Environment, Washington, DC. Crawford, R.L., Engstrom, T.R., 2001. Characteristics of avian mortality at a north Florida television tower: a 29-year study. Journal of Field Ornithology 72 (3), 380–388. FFWCC. (n.d.). Panther net. Retrieved from http://myfwc.com/panther Forman, R.T.T., 2000. Estimate of the area affected ecologically by the road system in the United States. Conservation Biology 14 (1), 31–35. Forman, R.T.T., Deblinger, R.D., 2000. The ecological road-effect zone of a Massachusetts (U.S.A.) suburban highway. Conservation Biology 14 (1), 36–46. Gebo, N., Brooks, R.P., 2012. Hydrogeomorphic (HGM) (and FQAI) assessments of mitigation sites compared to natural reference wetlands in Pennsylvania. Wetlands 32 (2), 321–331. Gibbons, J.W., 2003. Terrestrial habitat: a vital component for herpetofauna of isolated wetlands. Wetlands 23 (3), 630–635. Kettlewell, C.I., Bouchard, V., Porej, D., Micacchion, M., Mack, J.J., White, D., Fay, L., 2008. An assessment of wetland impacts and compensatory mitigation in the Cuyahoga River watershed, Ohio, USA. Wetlands 28, 57–67.
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Lane, C.R., 2000. Proposed ecological regions for freshwater wetlands of Florida. Masters Thesis, University of Florida, Gainesville, Florida, USA. Lane, C.R., Brown, M.T., 2007. Diatoms as indicators of wetland condition. Ecological Indicators 7, 521–540. Longcore, T., Rich, C., Mineau, P., MacDonald, B., Bert, D.G., Sullivan, L.M., et al., 2012. An estimate of avian mortality at communication towers in the United States and Canada. PLoS ONE 7 (4), e34025, doi:10.1371/journal.pone. Mack, J.J., 2006. Landscape as a predictor of wetland condition: an evaluation of the landscape development index (LDI) with a large reference wetland dataset from Ohio. Environmental Monitoring and Assessment 120, 221–241. Maehr, D.S., 1988. Florida panther movements, social organization, and habitat utilization, annual performance report, 7/1/87-6/30/88, study no. E-1-12 II-E-2 7502. Florida Game and Fresh Water Fish Commission, Florida. Margriter, S.C., 2011. Assessing the condition of Hawaiian coastal wetlands using a multi-scaled approach. University of Hawaii, Manoa, Hawaii (Masters thesis). Moreno-Mateos, D., Power, M.E., Comín, F.A., Yockteng, R., 2012. Structural and functional loss in restored wetland ecosystems. PLoS Biology 10 (1), e1001247, doi:10.1371/journal.pbio.1001247. Nestlerode, J., Engle, V.D., Bourgeois, P., Heitmuller, P.T., Macauley, J.M., Allen, Y.C., 2009. An integrated approach to assess broad-scale condition of coastal wetlands—the Gulf of Mexico Coastal Wetlands pilot survey. Environmental Monitoring and Assessment 150 (1–4), 21–29, doi:10.1007/s10661-008-0668669. Odum, H.T., 1996. Environmental Accounting: Emergy and Environmental Decision Making. John Wiley and Sons, New York. Ogawa, H., Male, J.W., 1986. Simulating the flood mitigation role of wetland. Journal of Water Resource Planning and Management 112, 114–128. Oliver, L., Lehrter, J., Fisher, W., 2011. Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands. Marine Ecology Progress Series 427, 293–302, doi:10.3354/meps09087. Parker, N.M., 1998. Spatial models of total phosphorus loading and landscape development intensity in a North Florida watershed. University of Florida, Florida (Masters thesis). Peterjohn, W.T., Correll, D.L., 1984. Nutrient dynamics in an agricultural watershed: observations on the role of a riparian forest. Ecology 65, 1466–1475. Reiss, K.C., 2006. Florida wetland condition index for depressional forested wetlands. Ecological Indicators 6, 337–352. Reiss, K.C., Hernandez, E., Brown, M.T., 2007. An evaluation of the effectiveness of mitigation banking in Florida: ecological success and compliance with permit criteria, Report submitted to Florida Department of Environmental Protection #WM-881 and US Environmental Protection Agency Region IV #CD 96409404-0. University of Florida, Florida. Reiss, K.C., Hernandez, E., Brown, M.T., 2009. Evaluation of permit success in wetland mitigation banking: a Florida case study. Wetlands 29 (3), 907–918. Reiss, K.C., Brown, M.T., Lane, C.R., 2010. Characteristic community structure of Florida’s subtropical wetlands: The Florida wetland condition index for depressional marshes, depressional forested, and flowing water forested wetlands. Wetlands Ecology and Management 18, 543–556, doi:10.1007/s11273-0099132-z. Reynolds, J.E., 2001. Urbanization and land use change in Florida and the south. Current issues associated with land values and land use planning. In: Proceedings of a Regional Workshop, SERA-IEG-30. SRDC Series #220, Mississippi, Mississippi State: Southern Rural Development Center. Ruhl, J.B., Salzman, J., 2006. The effects of wetland mitigation banking on people. National Wetlands Newsletter 28 (2), 1, 9–14. Story, G.A., Redmond, A., Ertman, D., Johnson, H., Hankla, D., Palmer, D., Carmody, G., Rieck, B., Ferrell, D., Moore, R., Gipe, T., Bain, A., 1998. Joint state/federal mitigation bank review team process for Florida. United States Army Corps of Engineers, Jacksonville District. Taylor, W.K., 1973. Black-throated blue and Cape May warblers killed in central Florida. Bird Banding 44 (4), 258–266. Vivas, M.B., 2007. Development of an index of landscape development intensity for predicting the condition of aquatic and small isolated palustrine wetland systems in Florida. University of Florida, Florida (Doctoral dissertation). Vivas, M.B., Brown, M.T., 2006. Areal empower density and landscape development intensity (LDI) indices for wetlands of the Bayou Meto Watershed, Arkansas. Report submitted to the Arkansas Soil and Water Conservation Commission under sub-grant agreement SGA 104. University of Florida, Florida.
Contents lists available at ScienceDirect
Ecological Modelling journal homepage: www.elsevier.com/locate/ecolmodel
Application of the landscape development intensity (LDI) index in wetland mitigation banking Kelly Chinners Reiss a,∗ , Erica Hernandez b,1 , Mark T. Brown a a b
Howard T. Odum Center for Wetlands, 100 Phelps Lab, Museum Road, University of Florida, Gainesville, FL 32611-6350, United States Department of Environmental Protection, Kissimmee Prairie Preserve, 33104 NW 192nd Avenue, Okeechobee, FL 34972, United States
a r t i c l e
i n f o
Article history: Available online 31 May 2013 Keywords: Florida Human disturbance gradient Landscape development intensity Wetland mitigation bank
a b s t r a c t The landscape development intensity (LDI) index, which can be used as a human disturbance gradient, is an effective tool in assessing location of wetland mitigation banks where large tracts of land are managed to protect wetland function by offsetting wetland losses from off-site land development. As part of a larger study to determine the effectiveness of mitigation banking in Florida, this article focuses on characterizing the landscape intensity of wetland mitigation banks in the state. Two scales of the LDI index were calculated: wetland assessment area scale LDI index (n = 58), which characterizes the landscape surrounding a small parcel of land within a mitigation bank boundary, and mitigation bank scale LDI index (n = 26), which characterizes the lands surrounding the entire boundary of the wetland mitigation bank. Approximately two-thirds of the wetland assessment areas (n = 38) had LDI index scores less than 2.0 (where 0.0 represents no human development), with a mean LDI index score of 3.2 ( = 4.9). LDI index scores were calculated such that all lands within the 100 m zone surrounding a wetland assessment area designated as restoration, enhancement, creation, or preservation were assigned LDI index scores reflecting natural lands. In this application, the LDI index score was considered a tool to predict potential wetland condition based on successful restoration. Bank scale LDI index scores, based on land use within the 100 m zone surrounding the entire mitigation bank, were generally higher than assessment area scores, with a mean bank scale LDI index score of 7.8 ( = 5.4) and a median of 6.5. At the wetland scale, evaluation of landscape condition using the LDI index presented a quantitative analysis tool to determine potential ecological lift (i.e. expected gain in ecological condition) with successful wetland restoration practices within the mitigation bank. Whereas at the bank scale, LDI index calculations suggested the expected impacts from human land use activities outside of the control of the bank property. We propose using the LDI index to facilitate calculation of wetland mitigation potential and credit awards for mitigation banks, and that regardless of the tool used consideration of potential wetland functional lift should incorporate a landscape perspective. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Historically, wetlands have been considered areas to conquer, with humans draining or filling wet areas to create lands more suitable to human land use activities; however, overtime, we have recognized the ecosystem services provided by wetland systems such as air and water purification, wildlife habitat, and storage of floodwaters, and efforts have been made to protect wetland systems. One mechanism to compensate for wetland loss that has
∗ Corresponding author. Tel.: +1 352 392 2424. E-mail addresses: kcr@ufl.edu (K.C. Reiss), echernandez@ufl.edu (E. Hernandez), mtb@ufl.edu (M.T. Brown). 1 Current address: Howard T. Odum Center for Wetlands, 100 Phelps Lab, Museum Road, University of Florida, Gainesville, FL 32611-6350, United States. Tel.: +1 352 392 2424. 0304-3800/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ecolmodel.2013.04.017
gained attention at the national scale over the past two decades is wetland mitigation banking. A wetland mitigation bank consists of a land area protected from human development activities and often actively managed for enhanced ecological function or ecological restoration. Because humans are known to damage and degrade natural environments, install impervious surfaces, and remove native habitat cover, state and federal regulations in the United States require avoiding, minimizing, and mitigating impacts to wetland ecosystems. In wetland mitigation banking systems, banks with restored, enhanced, created, or preserved land are established and awarded credits, which are purchased by land developers to compensate for impacts to off-site wetlands. Credits are determined based on expected lift, or a best professional judgment of the expected with-mitigation condition (an anticipation of the future condition) compared to the current condition. While there are requirements for establishing wetland mitigation banks ranging from funding, easements, service areas, and
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Fig. 1. Aggregate systems diagram of a wetland embedded within a developed landscape. The systems boundary is a 100-m buffer around the delineated wetland edge, which could be used for the landscape development intensity (LDI) index calculation.
land management, this paper addresses one of the nine criteria identified for establishing mitigation banks as identified in Florida Statutes Section 373.4136, F.S. requiring that banks be located adjacent to lands that will not adversely affect the viability of the wetland mitigation bank. Acknowledging the influence of the adjacent lands and understanding the landscape location of a wetland mitigation bank could inform credit award and expected long-term ecological condition. In order to quantify the landscape condition surrounding wetland mitigation banks, we propose using the landscape development intensity (LDI) index, which can be used as a human disturbance gradient. Owing to the unique position of wetlands in the landscape, receiving and filtering airborne and aquatic pollutants from nonpoint and direct point source inflows, the LDI index represents a human disturbance gradient for wetland systems based on nonrenewable energy use (e.g., fertilizer, fuel and electricity) in the surrounding landscape. An aggregate systems model of the inflows to a wetland embedded in a developed landscape is presented in Fig. 1. The LDI index reflects local human activity, combining the effects from air and water pollutants, physical damage, and changes in the suite of environmental conditions (e.g., nutrient load, water levels) on the structure, process, and function of ecosystems. The major advantages of a LDI index tool include the ease and speed of calculation, the widespread availability of digital data, the repeatable and objective evaluation of wetlands, and the capability to perform the assessment from the office, avoiding seasonal and other field constraints (e.g. time commitment, labor, costs, travel and field conditions). The LDI index calculates the amount of nonrenewable energy use weighted by area of land use within a geographic information systems (GIS) framework (Brown and Vivas, 2005). Natural, undeveloped land use/land cover (LU/LC) classes have a LDI index value of zero and low intensity recreational areas, unimproved native pastures, and open spaces have an LDI index around one. Agrarian land uses have slightly higher LDI index values ranging from 3 to 6
for managed pasture and 7–14 for other agricultural land uses (e.g. citrus, row crops, dairy production). Increasing energy use in low intensity urban LU/LC classes such as two-lane roads and singlefamily housing have an LDI index ranging from 20 to 27. The highest intensity land uses have LDI index values greater than 30 including institutional, commercial, industrial, multi-family housing, and commercial districts. The earlier LDI index presented by Brown and Vivas (2005) had a scale from 1 to 10; then a modified equation was developed by Vivas (2007) and later presented by Reiss et al. (2010) with a scale from 0 to 42, with 42 representing the highest land use development category in Florida, central business district average four stories. Some of the earliest work developing the LDI index concept related water quality in the St. Marks watershed in Florida to human development intensity (Brown, 1980; Brown et al., 1998; Parker, 1998). The LDI index has been used in several studies of Florida wetlands (e.g., Reiss, 2006; Lane and Brown, 2007; Nestlerode et al., 2009; Reiss et al., 2010) and also in other states (e.g., Minnesota – Bourdaghs et al., 2006; Ohio – Mack, 2006; Arkansas – Vivas and Brown, 2006) and more recently outside of the lower 48 states (e.g. Taiwan – Chen and Lin, 2011; Hawaii – Margriter, 2011; St. Croix – Oliver et al., 2011). As part of a larger study to determine the effectiveness of mitigation banking in Florida (Reiss et al., 2007), the specific objective of this paper is to calculate the LDI index for existing wetland mitigation banks to inform the assessment of wetland condition. Land areas within the wetland mitigation banks undergoing restoration, enhancement, creation, or preservation activities were assigned the development intensity of natural land. Clearly mitigation activities require nonrenewable energy use (e.g., earth moving activities such as ditch filling, exotic plant removal or herbicide treatment); however, the calculated LDI index values for the wetland assessment areas were considered “potential” LDI index, which would be attained at a restored, self-sustaining community given ecological project success. Thus, the calculated LDI index value reflects
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Fig. 2. Location of 29 wetland mitigation banks included in this study. Wetland region boundaries according to Lane (2000).
the lowest potential LDI index score and in turn the highest potential ecological condition, once mitigation activities are complete. In this way, the LDI index can inform best professional judgments on future wetland condition, an important step in awarding credit in wetland mitigation banking. 2. Materials and methods In total 29, wetland mitigation banks were included in this study located throughout the four Florida wetland regions (Lane, 2000). A single wetland mitigation bank was located in the panhandle wetland region. Just over half of the study mitigation banks (n = 15) were within the central wetland region, with 10 in the south wetland region, and three in the north wetland region (Fig. 2). This distribution reflects the higher population density of south and central Florida and the more rapid development of lands in those regions based on market demand for wetland credits. Detailed information on mitigation banks in Florida including acreage, credits awarded, credits released, and contact information, can be accessed on the web (http://www.dep. state.fl.us/water/wetlands/mitigation/mitigation banking.htm). Within each bank, smaller wetland assessment areas were identified for evaluation. While some banks were relatively small in land area and homogeneous in wetland community type, many covered large land areas and contained a variety of wetland community types. The number of wetland assessment areas selected depended on a combination of site-specific conditions such as homogeneity of wetland community types, mitigation activities completed to date, and progress toward permit success criteria; area of wetland; type of mitigation (e.g., restoration, creation, enhancement, or preservation); and general site conditions. The specific area for each wetland assessment area was determined at the time of field visits, which occurred within the context of the larger project by Reiss et al. (2007) and not specifically for this GIS based analysis. Selection of wetland assessment areas was based on site
conditions and physical access as determined by the land manager and/or mitigation bank owner. In general, wetland assessment areas represented an example of current wetland condition and were not expected to have a 1:1 relationship to the condition of the entire wetland mitigation bank. That is, wetland assessment areas provide insight to smaller land areas but are not meant as a surrogate to represent condition within the entire mitigation bank. Another important note perhaps of interest to the reader, many wetland mitigation banks in Florida include a mosaic of upland and wetland habitats, necessitating delineation of smaller wetland assessment areas within a bank to inform understanding of wetland condition. Two scales of the LDI index were calculated: wetland (assessment area) scale LDI index (n = 58) and (mitigation) bank scale LDI index (n = 26). To calculate the wetland scale LDI index, a 100 m zone was delineated around the edge of each wetland assessment area and LU/LC classes within the zone were identified based on 2004 digital orthographic quarter quads and field notes for current surrounding land use. To calculate the bank scale LDI index, a 100 m zone was identified around the bank boundary and LU/LC classes within the zone were identified using 2000 land use cover maps, available from the Florida Geographic Data Library (http://www.fgdl.org/). Only 26 of 29 bank scale LDI calculations were completed as the mitigation bank outline was not available for one bank; two phases of one bank were combined into one bank scale LDI index; and year 2000 land use was not available for the bank in the panhandle. 2.1. Calculation The LDI index provides a quantitative measure of nonrenewable energy use per unit area of landscape surrounding a specific point or polygon. Non-renewable resources are defined as resources that are not renewed on a time scale relevant to the average human life span. Since all resources have differing energy quality (Cleveland,
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Table 1 Surrounding land use classification for wetland mitigation bank sites based on broad landscape development intensity (LDI) index categories. Note these are not the actual dominant land uses surrounding a particular site, but a representation of the approximate land use based on the LDI index value. General land use/land cover category
Natural lands Minimal disturbance Low intensity disturbance – open space, native pasture Managed pasturelands Agriculture – citrus, row crops, dairy Mixed agricultural and/or low intensity urban
LDI index range
0.0 >0.0 to ≤0.5 >0.5 to ≤3.0 >3.0 to ≤6.0 >6.0 to ≤14.0 >14.0 to ≤18.5
2012) or ability to do work, calculation of the LDI index includes quantifying the nonrenewable energy in units of the same energy form, customarily solar energy. Odum (1996) used the term emergy, the expression of different forms of energy converted to a single form of energy, in early innovative work in environmental accounting. Emergy is the amount of available energy of one form required to produce a flow of material, energy, or information (Odum, 1996). Emergy can be coupled to flows of resources and to storages or stocks. When resource flows (i.e. energy per time, or power) are expressed in their emergy equivalents the resulting quantity is termed empower, or emergy per time. When empower is computed on an areal basis (i.e. sej time−1 area−1 ) the resulting quantity is termed areal empower intensity (AEI). The LDI index employs the AEI of land uses, converting them to a non-dimensional index (Brown and Reiss, 2010). The LDI index is calculated using ESRI® ArcMapTM 10.0 and a spreadsheet software program (e.g. Microsoft Office 2010 Excel software). In general, the method involves identifying the sampling point of interest, digitizing a polygon around the point of interest, clipping the LU/LC layer within the boundary of the polygon, summing the area-weighted AEI values of each LU/LC class within the polygon, and computing the LDI index score. In order to calculate the LDI index several resources are needed including digital imagery (e.g. digital orthophoto quarter quad (DOQQ) imagery, high resolution digital imagery), a land use/land cover (LU/LC) shapefile, and a look-up table for the AEI value for each LU/LC class. The LDI index value is calculated as: LDI = 10 × log10
AEI
Total
AEIRef
(1)
where LDI, LDI index for a given polygon, AEITotal , total areal empower intensity (AEI) within the polygon (including the background environment), and AEIRef , renewable AEI of the background environment within the polygon. The total areal empower intensity (AEITotal ) is calculated as follows: AEITotal = AEIRef +
(%LUi × AEIi )
(2)
where LUi , the fraction of the total delineated zone in land use i, and AEIi , the nonrenewable areal empower intensity for land use i. 2.2. Non-renewable areal empower intensity Reiss et al. (2010) present the non-renewable AEI values for LDI index categories for Florida. Some additional AEI values have been calculated for different states, but most states currently lack AEI values for statewide LU/LC classes. While many users may rush to calculate the LDI index with any available AEI values, we offer a word of caution. The first step in developing a reliable LDI index tool for a state is to begin with a complete emergy evaluation of the state and all major human land use activities (e.g. specific farming crops, cattle stocking densities, residential development density, commercial land uses). While there may be some overlap
Wetland Scale
Bank Scale
Count (n)
Percent (%)
Count (n)
Percent (%)
16 15 10 3 12 2
28 26 17 5 21 3
1 2 2 6 11 4
4 8 8 23 42 15
in land use intensity across regions, these activities have variable inputs and requirements depending on local factors and the natural background environment. The AEIRef used for Florida’s background environment is 1.99 E15 sej ha−1 yr−1 . 3. Results Mean bank area was 848 ha ( = 894.5 ha) with a range from 27 ha at Graham Swamp to 3653 ha at Everglades Mitigation Bank/Phase II. The size of the wetland assessment areas ranged from 0.2 ha at Lake Monroe to 282.2 ha at Loxahatchee, with a mean wetland assessment area of 20.3 ha ( = 48.3 ha). The mean wetland assessment area was 15.8% ( = 30.8%) of the total mitigation bank area, with a range spanning from 0.1% at Loblolly Mitigation Bank and TM-Econ to 100% at Bear Point and Graham Swamp. Less than 1% of the bank area was included in the wetland assessment areas at 13 banks, while over 50% of the bank area was included in the wetland assessment areas at four banks. The mean wetland scale LDI index score was 3.2 ( = 4.9) with a median of 0.3 and a range from 0.0 to 16.7. The distribution of LDI index scores was non-normal (Shapiro–Wilk W = 0.7029, p < 0.01), with 16 assessment areas with wetland scale LDI index scores of 0.0 (Table 1). An additional 25 wetland scale LDI index scores were less than 3.0, so that over 70% of wetland scale LDI index calculations were below 3.0, which roughly suggests low levels of human disturbance in the immediately adjacent lands at the wetland scale. Approximately 5% of wetland scale LDI index scores were between 3.0 and 6.0, and the remaining 25% were greater than 6.0, representing impacts from agricultural to low intensity urban land uses. Bank scale LDI index scores were higher, with a mean bank scale LDI index score of 7.8 ( = 5.4), a median of 6.5, and a range from 0.0 to 18.2. One mitigation bank had a bank scale LDI index score of 0.0 (Table 1); this mitigation bank is an island situated in an aquatic preserve. Six banks had a range of bank scale LDI index scores from 3.0 to 6.0. In contrast to the wetland scale, the majority of the bank scale LDI index scores were greater that 6.0 (57%). A weak correlation was found between wetland scale and bank scale LDI index scores (r = 0.27, p < 0.05). Fig. 3 shows several significant discrepancies between wetland scale and bank scale LDI index scores. In particular, some wetland assessment areas have significantly higher wetland scale LDI index scores due to proximity to interior paved roads or built structure (e.g. offices, recreational facilities). The impervious surfaces are considered permanent structures within the footprint of the wetland mitigation area. On the other end of the scale, some wetland scale calculations had smaller LDI index scores than the bank scale LDI index, often because of the interior location of the wetland assessment areas, buffered by the mitigation lands from the impacts of roads or other human land uses occurring along the bank property boundaries. 4. Discussion The application of the LDI index to mitigation sites presents many challenges. Primarily, there is concern over how to assign AEI
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Fig. 3. Wetland scale (light blue bars) and bank scale (dark green bars) landscape development intensity (LDI) index scores. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
values to lands that in the past have been used for human activities but currently are being restored or enhanced to a more natural condition. As such, wetland scale LDI index calculations used in this study have been considered “potential” LDI, suggesting the wetland condition attainable based on successful mitigation activities. Further, differences between wetland and bank scale LDI scores raise the question as to the most important or relevant scale for considering impacts from human disturbance to wetland systems. 4.1. Sustainability and landscape position Much of the land area, especially in proximity to wetlands, has been cross ditched and drained since human settlement began in Florida. Understanding the limitations of landscape location in with-mitigation scenarios is necessary when assessing lift (i.e. improvement in ecological condition given completion of mitigation activities) in mitigation bank credit awards. A bank surrounded by developed land uses or located in areas that are expected to support human development activities in the future should not anticipate full credit award for with-mitigation scenarios, as the anticipated ecological function would vary from the reference standard (i.e. wetlands not located near human development activities). Consideration of potential wetland functional lift should incorporate a landscape perspective, which could include application of available tools such as variable scales of the LDI index, reflecting local and broad scale landscape support for wetland condition. Within a highly developed country, finding a significantly large tract of land that wholly insulates mitigation wetlands from human impacts may be impossible. For example, Forman (2000) suggests that approximately 20% of the land area within the United States has experienced direct ecological effects from the public road system alone. This 20% figure, which does not include secondary or indirect impacts, is expected to rise in the future, suggesting that roadways will continue to act as a major influence on community structure and wildlife habitat suitability within wetland mitigation banks. For example, mitigation banks adjacent to or bisected by highways is a concern as highways act as significant barriers for wildlife dispersal. In a study on the mortality of amphibians, reptiles, and other wildlife along a two-lane paved causeway, Ashley and Robinson (1996) found over 32.000 individuals died in two two-year periods along the road. In another study from Boston, Massachusetts, researchers found the impacts of a major four-lane
road extended at least 100 m and perhaps more than 1 km for some effects (Forman and Deblinger, 2000). Further examples of the significant impacts that roads play in disturbing wildlife include the mortality of herpetofauna along US-27 in Lake Jackson, Florida (Aresco, 2005), which are a commonly overlooked though major biotic component of freshwater wetlands (Gibbons, 2003). In addition, there may be impacts on the largest protected species. In one example, Maehr (1988) found that female Florida panthers (Felis concolor coryi) rarely cross major roads or use underpasses, so their habitat is still essentially fragmented by roads even where underpasses have been constructed (Maehr, 1988, as cited by the Florida Fish and Wildlife Conservation Commission, n.d.). These studies suggest that roadways and conservation areas should be well separated, and yet many mitigation banks border busy roadways or busy roads bisect them. A further concern regarding landscape support is the presence of tall transmission towers on or near bank properties that may occur within avian flight paths. Species attracted to mitigation banks may be endangered by neighboring structures. For example, in a study on bird mortality in central Florida, Taylor (1973) found hundreds of black-throated blue and Cape May warblers killed in a six-week period in September and October at the WDBO-WFTV TV Tower, in the autumns of 1969–1972. In a more recent study by Crawford and Engstrom (2001), 44,007 individuals of 186 species were collected at the WCTV television tower in north Florida, and over 94% of the total number of individuals was neo-tropical migrants. The study spanned a 29-year period, one of the longest of its kind. They found that towers approximately 94 m or lower might not pose as great a threat to avian mortality as caused by towers 200 m or greater. While many of the supporting studies are several years to decades old, the rapid increase in transmission towers across the urban and rural landscape suggests such findings are even more significant today. For example, in a recent meta-analysis of bird mortality from communication towers, Longcore et al. (2012) reported approximately 4500 towers greater than or equal to 150 m in height and over 70,000 towers greater than or equal to 60 m in height across the continental US and Canada. A recent study on the demographics of Florida mitigation banks suggested that the location of banks in more rural areas redistributes wetland resources and the associated ecosystem services away from urban areas and thus removes some of the services afforded by these systems (Ruhl and Salzman, 2006). Locating banks within developed urban areas may improve the
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distribution of certain ecosystem services across the landscape (e.g., flood attenuation), but such a location is not expected to reproduce the suite of ecosystem services attained in a more natural landscape setting. A national study by Brown and Lant (1999) found that the spatial location of banks was often in downstream or coastal locations, so that replacement may not be providing equal function compared to wetlands lost in the upper watershed. While the contrast between impacted site location and mitigation bank location is not a focus of this study, it does warrant further investigation. In one recent study Gebo and Brooks (2012) describe most natural wetlands in Pennsylvania as riparian systems with most contributing to headwater streams; the authors point to findings by Brody et al. (2008) and Kettlewell et al. (2008) that most mitigation wetlands are isolated depressions. In this case, because wetland mitigation allows trading riparian wetlands for isolated depressions, we expect a shift in the functions (e.g. water purification, wildlife habitat, water storage, flood attenuation) expressed in the mitigated wetland types compared to those being lost in development. For example, significant loss in wetland function from relocating mitigation wetlands toward the bottom of a watershed may manifest in loss of flood control (Ogawa and Male, 1986 as cited by Brown and Lant, 1999) and change in water quality benefits (Peterjohn and Correll, 1984), which should be taken into consideration. Wetland mitigation must be considered a trade-off between temporal and spatial ecosystem function, and we must maintain realistic expectations of attainable function in the calculated withmitigation scenario in light of impacts from roadways, transmission towers, wildlife behavior, and unmentioned but more obvious impacts like pesticide over-spray and emissions from fossil fuel combustion. That is, when a bank is adjacent to developed lands, the location and landscape functional component should never be awarded full credit for landscape location. 4.2. Ecological integrity Before compensatory wetland mitigation is considered, state and federal regulations propose that first wetland impacts are avoided and second that unavoidable wetland impacts are minimized. Remaining wetland impacts are then mitigated with the intention of replacing lost wetland function and achieving the mandated no net loss of wetland function. The impetus for wetland protection comes from protecting the physical, biological, and chemical integrity of our Nations waters, particularly in matters of human health and economic concerns (e.g., coastal fisheries, navigation). Defining ecological integrity of a delineated wetland or water body can be done in a number of different ways, each reaching a somewhat different conclusion depending on what was measured and how it was quantified. There is currently no single scientifically agreed upon best method to assess the ecological integrity of an ecosystem. However, developing a repeatable and objective measure of ecological integrity or a surrogate for integrity (e.g. condition) that is easy to implement and unambiguous would be ideal. Wetland assessment areas in this study located in mitigation banks that had achieved final permit success criteria and had all potential credits released did not receive the highest possible scores using field assessment methods (Reiss et al., 2007), suggesting full wetland function had not been attained at the time of credit release or thereafter. Some banks near busy highways, receiving polluted water (e.g., receiving water from a canal that receives urban stormwater runoff), or adjacent to high intensity human development activities (e.g., high-density single family residential), were assumed in the permit to have the potential to provide full wetland function (Reiss et al., 2009). However, the landscape of Florida has become more urban and Florida has one of the highest
rates of conversion of rural to urban land use (Reynolds, 2001). Realistic with-mitigation scenarios should be of primary importance for determining potential credit awards for a mitigation bank. When permits assume that the with-mitigation scenario attains full wetland function and success criteria are established based on assumed attainment of full wetland function, any wetland (or upland) community in a bank that falls short of full wetland function represents a potential net loss of wetland function, which is of critical national concern. In a recent meta-analysis of global wetland restoration projects MorenoMateos et al. (2012) conclude that current restoration projects have limited performance (e.g. structure, biochemical processing), and if we proceed with current restoration we will experience an increasing global loss of structure and function of wetland ecosystems. 4.3. Credit potential Florida rules recognize that not all mitigation areas are expected to attain “reference condition.” Chapter 62.312.350, F.A.C. states that “it is not the intent of the Department to require that the mitigation area exactly duplicate or replicate the reference water.” Thus, an important concept in evaluating mitigation success is an understanding of how a mitigation site is assessed for credit. In Florida, a mitigation credit is defined as a “standard unit of measure which represents the increase in ecological value resulting from restoration, enhancement, preservation, or creation activities” (Section 373.403(20), F.S. and Chapter 62-342.200(5), F.A.C.). Typically, the mitigation area is divided into polygons or assessment areas of similar condition, treatment, community type, and anticipated results. Each area is assessed for anticipated functional lift between the current or predicted without bank condition compared to the anticipated with bank condition (Story et al., 1998). In reviewing permit files and attachments for Florida mitigation banks, it was clear that the with-mitigation bank scenario was scored high in anticipation that full function would return to a site once mitigation activities were completed (Reiss et al., 2009). This was true even in cases where the surrounding landscape would have an impact on water quality or quantity or where wildlife support or movement was significantly curtailed. It often seemed that the assessment was focused only on the anticipated capacity to support vegetation rather than the full suite of integrated wetland functions of the community. This practice leads to an over-estimation of ecological lift and elevated mitigation credit award. 5. Conclusion Return of full wetland function may be an impossible goal given current and future human development activities across the landscape. A more realistic outlook on wetland mitigation outcomes would probably reduce the amount of potential credits allocated for a particular site. Some may argue that this would reduce the economic incentive for mitigation banking; however, economic evaluation is beyond the scope of this study and existing wetland regulatory rules. Many wetland mitigation banks were limited by their position in the landscape as identified quantitatively by high bank scale LDI index values. The result of being located in a developed landscape may manifest as managed hydrologic regimes and altered benefits to downstream systems, water quality concerns, or other barriers to attaining full function. Using a quantitative, objective index of landscape condition like the LDI index to assess mitigation credit potential for the landscape location setting could inform the calculation of with-mitigation credit awards.
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