RAPIDLY AND REMOTELY ASSESSING THE CONDITION OF HAWAII’S COASTAL WETLANDS
Sandra C.Margriter
M.S. Thesis Proposal
Advisor: Dr. Greg Bruland
Department of Natural Resources & Environmental Management
2/15/2010
Coastal wetlands are among the most productive ecosystems on Earth and provide a wide range of ecological services such as wildlife habitat, nutrient cycling, waste “removal,” and shoreline protection from storms. Conservatively, it is estimated that over 30 percent of natural lowland wetlands in Hawaii have been lost and that the remainder have been seriously degraded from altered hydrology and invasion of non-native species. In order to comprehensively assess the functional integrity of wetlands, the U.S. Environmental Protection Agency (EPA) advises using a suite of metrics including Level I (remote methods), Level II (rapid field-based), and Level III (intensive field-based) assessments. Remote assessments, such as landscape development intensity (LDI) indices, can provide a quantitative measure of human development and the potential impacts on the biological, chemical, and physical processes of surrounding lands or waters. In contrast, rapid assessment methods use field indicators to identify ecosystem stressors and to evaluate the functional integrity of wetlands. While some research show correlations between ecosystem degradation and land-use impacts at the watershed scale, others show stronger correlations between wetland health indicators and landscape disturbances within smaller buffer areas. This study will calculate LDI indices for the watersheds as well as the 100-m and 1,000-m buffer zones surrounding each wetland. In order to determine the strength of the relationship between LDI indices and the condition of coastal wetlands in Hawaii, 26 wetlands will be evaluated using three different rapid field assessment methods. In addition, remote and rapid condition assessment scores will be analyzed and compared with the water quality, soil properties, and δ15N values measured in the wetlands during previous intensive field surveys. In short, the project will evaluate the capacity of remote and rapid assessment methods to detect a gradient in the functional integrity of Hawaiian coastal wetlands and the landscapes that contain them.
Introduction
Wetlands are among the most productive ecosystems on Earth. In addition to providing unique plant communities and wildlife habitat they act as a filter to cleanse polluted waters, buffer coastlines from storms, recharge groundwater, and attenuate floods. These ecosystem services were estimatedto be worth approximately $20,000/ha/yr(Constanza 1997) to human welfare.
In 1990s the concept of “no net loss” of wetlands became a unifying and seemingly simple goal within the United States. This led to natural resource protection laws requiring wetlands to be created or restored when wetlands are lost as a result of human land use and development. On paper, the U.S. Army Corps of Engineers’ implementation of “no net loss” appears to be working. However, few statistics exist on what functions (or ecosystem services) are lost or gained when wetlands are damaged, created, or restored (Mitsch and Gosselink 2007).
Conservatively, it is estimated that over 30 percent of natural lowland wetlands in Hawaii have been lost and that the remainder have been seriously degraded from altered hydrology and invasion of non-native species (Erickson and Puttock 2006). The ecological processes of these fragile ecosystems continue to be threatened by human development. Freshwater discharged into coastal wetlands is degraded by adjacent and upslope anthropogenic disturbances (HawaiiWatershedAtlas.com/Watersheds; Bruland and MacKenzie 2010) and increased water withdrawal rates needed to meet the growing demands of resident and tourist populations are reducing the volume of groundwater discharged along coastlines (Oki 1999). Furthermore, endangered wetland species are declining in Hawaii, which is at least partially due to the loss of wetland habitat (Strategic Plan for Wetland Conservation in Hawaii, Review Draft Henry 2005).
In order to comprehensively assess the functional integrity ofwetlands, the U.S. Environmental Protection Agency (EPA) advises using a suite of metrics including Level I (remote), Level II (rapid field-based), and Level III (intensive field-based) assessments (Faber-Langendoenet al. 2008). While indices of biotic integrity (IBI) are extremely valuable and provide the most detailed analysis of habitat condition, they require tremendous effort and are costly. Rapid and remote assessment procedures, on other hand, are more expedient and are correlated with vegetation field surveys (Mack 2006) and water quality data (Brown and Vivas 2005).
Remote condition assessments such as landscape development intensity (LDI) indiceshave been usedforestimating the cumulativeimpacts from human disturbances at various spatial scales (Cohen et al. 2004, Brown and Vivas 2005, Mack 2006). The indices have provided a quantitative measure of human development and the potential impacts on the biological, chemical, and physical processes of surrounding lands or waters (Brown and Vivas 2005).Rapid assessment methods, on the other hand, use easily identifiable field indicators of ecosystem health (e.g. structural patch complexity) and ecosystem stress (e.g. anthropogenic disturbances) to assess the functional integrity of wetlands in relation to reference wetlands considered to be intact well functioning ecosystems (Fennessy et al. 2004, Faber-Langendoen et al. 2008).
The condition of landscapes and the ecological communities within them are strongly related to levels of human activity(Brown and Vivas 2005). While some research has shown a correlation between wetland degradation and land-use impacts at the watershed scale (Roth et al. 1996), others have shown stronger correlations between wetland health indicators and landscape disturbances within smaller buffer areas: 100m (Cohen 2004, Brown and Vivas 2005), 1,000m (Mack 2006), and 4,000m (Houlahan and Findley 2008).Recent research on 34 coastal wetlands in Hawaii suggests a stronger positive correlation of δ15N values in plant tissue with percent development within a 1,000-m radius than with population densities at the watershed scale (Bruland and MacKenzie 2010).
During the course of this project the condition of 26 coastal wetlands on the islands of Oahu, Maui,and Hawaii will, for the first time, be evaluated using rapid field assessments and landscape development intensity indices. In addition, the condition of the watersheds and buffers surrounding the wetlands will be remotely assessed using landscaped development indices. The remote and rapid assessment scores will then be compared with (Level III)soil and water data collected during prior field surveys (Bantilan-Smith et al. 2008, Bruland and DeMent 2009, Bruland and MacKenzie 2010). This approach will test the effectiveness of different remote (Level I) and rapidassessment methods (Level II) in detecting a gradient in the condition of Hawaiian coastal wetland ecosystems.
Objectives and Hypotheses
Research Question I:Are landscape development intensity indicescorrelated with the condition of coastal wetlands in Hawaii?
Hypothesis 1: Landscape development intensity indices arenegatively correlated with wetland rapid assessment scoresas human land uses are expected to directly and indirectly impact thefunctional integrity of wetlands.
Hypothesis 2: Landscape development intensity indices arenegatively correlated with soil bulk density, soil organic carbon, total nitrogen (H2O), total phosphorus (H2O), and δ15N isotope values,as human land use activities are expected to impact the water quality and soil properties of wetlands.
Research Question II: Are rapid assessment scores correlated with the quality of the soil and water, measured during intensive field surveys?
Hypothesis 1: Rapid field assessment scores provide a good indication of ecosystem integrity and are therefore correlated with water quality (nitrogen and phosphorus content), soil bulk density,soil organic carbon, and nitrogen source (δ15N values), measured during intensive field surveys.
Research Question III: Arespecific metrics correlated with wetland condition?
Hypothesis 1: Wetland condition assessment scores are negatively correlated with impervious surface area within the watershed and wetland buffer zonesas these surfaces significantly alter hydrology at local and regional scales.
Hypothesis 2: Wetland condition assessment scores are negatively correlated with distance to nearest road as even a single road can increase invasive species, decrease water quality and interfere with the natural flow of water.
Hypothesis 3: In addition to providing a greater diversity of ecosystem services, large wetlands can buffer the impacts of nutrient runoff and are therefore expected to have higher condition assessment scores.
Methods
Rapid and remotemethodswill be used to assess the condition of26 coastal wetlands on the islands of Oahu, Hawaii, and Maui where intensive field data are available from prior research(Bantilan-Smith et al. 2008, Bruland and DeMent 2009, Bruland and MacKenzie 2010). The sites include created, restored, and natural wetlands and representatives of isolated and developedwetlands across a spectrum of freshwater, brackish, euhaline, and hyperhalineconditions (Bruland and MacKenzie 2010).In order to utilize the proposed rapid assessment methods, the sites will first be classified into the following hydrogeomorphic classes: tidal, riverine, or depressional.
The wetland boundaries will be based on existing GIS layers of wetlands (USFS 1976) and water bodies (USGS 2000). The wetlandassessment areas (WAA) willbe defined using guidelines provided by the CRAM User Guide (Collins et al. 2008). Statistics onwetland area, distance to nearest road (USGS 1997), as well as the percentage of impervious surfaces (NOAA 2005) within buffer areas (100 and 1,000-m) and watersheds will be calculated in a GIS.
Landscape development intensity values will also be calculated for the watersheds and buffer zones (100 and 1,000-m)using the following data sources:watershed boundaries (Hawaii Office of State Planning),land cover (NOAA C-CAP 2001 and 2005),and County Zoning data (2005). The LDI values will quantify human disturbances along a continuous gradient, from 1, for Natural Systems, to 10 for a Central Business District (Brown and Vivas 2005). The final LDI value for each watershed will be area-weighted using the equation: LDItotal = %LUi · LDIi.
Three rapid assessment methods will be used to assess the condition of the wetlands: Wetland Rapid Assessment Procedures (WRAP), California Rapid Assessment Methods (CRAM), and the draft Hawaii Method. Of the three methods WRAPis the most rapid and least quantitative, whereas the Hawaii method is more detailedand labor intensive.The WRAP(Miller and Gunsalus 1997) and CRAM (Collins et al. 2008) scores provide a measure of the overall health or condition of a wetland, while the Hawaii HGM method (Science Applications International Corporation 2004, unpublished) more specifically addresses the integrity of individual functions and has different score sheets for each HGM class. Table 1 shows the functional integrity categories for assessing estuarine wetlands using the Hawaii HGM method compared with CRAM and WRAP. The final rapid assessment scores are calculated oncontinuous gradients: 1-3 (WRAP),1-120 (Hawaii HGM), and 1-100 (CRAM).
WRAP (Condition Assessment / CRAM (Condition Assessment) / Hawaii HGM (Tidal Fringe Wetland)Functional
Wildlife Utilization / Landscape Connectivity / Dissipation of Energy
Wetland Canopy / Hydrology / Retention of Imported Elements
Wetland Ground Cover / Physical Structure / Characteristic Plant Community
Habitat Support Buffer / Biotic Structure / Characteristic Invertebrate Food Webs
Hydrology / Characteristic Vertebrate Habitats
Water Quality Input / Habitat for Threatened and Endangered Species
Table 1. List of categories that will be used in rapidly assessing the condition and function of coastal wetlands.
Statistical Analysis
Regression analysis will be used in determining the strength of the relationship between:
- Landscape development intensityindex andrapidcondition assessment score (determined from condition metrics)
- Landscape development intensity indexandmean soil bulk density, soil organic carbon (Bruland and DeMent 2009), total phosphorus (H2O), total dissolved nitrogen (H2O)and δ15N values (Bruland and MacKenzie 2010)
- Rapid assessment score with mean soil bulk density, soil organic carbon (Bruland and DeMent 2009), total phosphorus (H2O), total dissolved nitrogen (H2O),and δ15N values (Bruland and MacKenzie 2010)
- Specific metrics (percent impervious surfaces, distance to nearest road and wetland area)with rapid assessment scores
In addition,multiple regression analysis will be used to analyze the combined influences of landscape development and wetland area onthe functional and conditional integrity of wetlands (e.g. Level II and Level III metrics).Before testing the correlation between rapid assessment scores and surrounding land-use, the metrics for anthropogenic stressors will be subtracted from the rapid assessment scores. This is necessary in order to avoid artificially increasing correlation coefficients between wetland condition and surrounding land-use.
Expected Results
Ecosystem degradation has been linked to urban as well as agricultural land use (Roth et al. 1996, Brown and Vivas 2005, Mack 2006, Houlahan and Findley 2008, Bruland and MacKenzie 2010). While land cover mapsadequately delineate generalizedvegetation categories (i.e. forest, shrubland, grassland) and development categories (low, medium, and high density development) they do not accurately map humanland use activities. For example, the land cover map of Hawaii Island(NOAA 2001) makesno distinction between natural grassland and pastureland. In this case, whenzoning data (agriculture)were combined with land cover data the LDI index calculated for the watershed increased from 1.1 to 2.8. Remote assessments using LDI indices derived from land cover and zoning data combined are therefore expected to provide a more comprehensive and accurate evaluation of human land use (i.e. development intensities).
Human land uses areexpected to negatively impact the functional integrity of wetlands and therefore LDI scores are expected to be negatively correlated with indicators of wetland health. An initial pilot study using WRAP to assess the condition of six coastal wetlands in Hawaii indicated that WRAP scores decreased as LDI indices increased (Figure 2). In aseparate pilot study of twenty wetlands, the development intensity indices were related to soil bulk density and water quality (e.g. total dissolved nitrogen and phosphorus), particularly when zoning data were used in conjunction with land cover to assign LDI values (Table 2).
However, in contrast to research in Florida (Brown and Vivas 2005) and Ohio (Mack 2006), the development intensity indicators were poor predictors of native species cover in the Hawaiian coastal wetlands. The long history of human land use and the prevalence of invasive species likely overwhelm the influences of current development intensity on the native plant composition. Of the 34 coastal wetlands surveyed by Bantilan-Smith et al (2008) only 16 of 85 plant species were identified as native. The dominant presence of invasive species suggests that it will be difficult to locate reference wetlandsthat are representative of culturally unaltered or “best attainable” conditions. This, however, is not entirely unusual since reference wetlands in nearly pristine condition exist in only a few ecoregions (Sutula 2006).
The strongest correlation coefficient (Spearman’s r = 0.75, p < 0.001) was calculated between watershed-scale development intensities and total dissolved nitrogen (Table 2). These results were inconsistent with research in Hawaii (Bruland and MacKenzie 2010) as well asin Florida(Brown and Vivas 2005) and Ohio (Mack 2006)which showed thathuman land use in closer proximity to wetlands was more strongly correlated with wetland health indicators.The steep, small watersheds in Hawaii may result in a stronger connection between the intensity of development within the watershed and the functional integrity of wetland ecosystems.
These results suggest that a watershed approach using LDI indices for ecosystem evaluation may be particularly appropriate in mountainous tropical islands such as Hawaii. Rapid assessment methods developed for various locations in the conterminous U.S. have provided sound quantitative information on the status and trends of wetland resources with relatively small investments of time and effort (Fennessy et al. 2004). This project, however, will be the first to evaluate the capacity of remote and rapid assessment methods to detect a gradient in the functional integrity of Hawaiian coastal wetlands and the landscapes that contain them.
Landscape Development / Area Calculated / Response / R2 / P-valueLDI Calculated From 30-m Land Use Data / 100-m buffer / Soil BD / 14.2% / 0.101
1,000-m buffer / Soil BD / 31.6% / 0.012
Watershed / Soil BD / 33.0% / 0.008
100-m buffer / Water, TDN / 11.2% / 0.149
1,000-m buffer / Water, TDN / 22.3% / 0.036
Watershed / Water, TDN / 40.3% / 0.003
100-m buffer / Water, TP / 19.6% / 0.051
1,000-m buffer / Water, TP / 22.6% / 0.034
Watershed / Water, TP / 32.6% / 0.009
100-m buffer / % Native / 0.1% / 0.896
1,000-m buffer / % Native / 0.0% / 0.932
Watershed / % Native / 0.0% / 0.926
LDI Calculated From 30-m Land Use and Zoning Data Combined / 100-m buffer / Soil BD / 21.4% / 0.04
1,000-m buffer / Soil BD / 40.6% / 0.003
Watershed / Soil BD / 35.3% / 0.006
100-m buffer / Water, TDN / 27.5% / 0.018
1,000-m buffer / Water, TDN / 36.4% / 0.005
Watershed / Water, TDN / 46.4% / 0.001
100-m buffer / Water, TP / 33.7% / 0.007
1,000-m buffer / Water, TP / 21.9% / 0.037
Watershed / Water, TP / 24.4% / 0.027
100-m buffer / % Native / 0.1% / 0.875
1,000-m buffer / % Native / 1.5% / 0.601
Watershed / % Native / 0.2% / 0.858
Literature Cited
Bantilan-Smith M, Bruland GL, MacKenzie RA, Henry AR, Ryder CR (2009) A Comparison of the Vegetation and Soils of Natural, Restored, and Created Coastal Lowland Wetlands in Hawaii. Wetlands 29:1023-1035.
Brown M, Vivas MB (2005) Landscape Development Intensity Index. Environmental Monitoring and Assessment 101:289-309.
Bruland GL, Bliss CM, Grunwald S, Comerford NB, Graetz DA (2008) Soil Nitrate-Nitrogen in Forested Versus Non-Forested Ecosystems in a Mixed Use Watershed. Geoderma 148:220-231.
Bruland GL, DeMentG (2009)Phosphorus Sorption Dynamics of Hawaii's Coastal Wetlands.Estuaries and Coasts 32:844-854.
Bruland GL, MacKenzieRA (2010)Nitrogen Source Tracking with δ15N Content of Coastal Wetland Plants in Hawaii.Journal of Environmental Quality 39:409-419.
Cohen MJ, Carstenn S, Lane CR (2004) Floristic Quality Indices for Biotic Assessment of Depressional Marsh Condition in Florida. Ecological Applications 14(3): 784-794.
Collins JN, Stein ED, M. Sutula, R. Clark, A. E. Fetscher, L. Grenier, C. Grosso, and A. Wiskind(2008) California Rapid Assessment Method (CRAM) for Wetlands, v. 5.0.2.
Constanza R, d’Arge R, Groot R, Farber S, Grass M, Hannon B, Limburg K, Naeem S, O’Neill RV, Paruelo J, Raskin RG, Sutton P, Belt PS, Belt M (1997) The Value of the World’s Ecosystem Services and Natural Capital. Nature 387:253-260.
Erickson TA, Puttock CF(2006) Hawaii Wetland Field Guide. U.S. Environmental Protection Agency.
Faber-Langendoen D, Kudray G, Nordman C, Sneddon L, Vance L, Byers E, Rocchio J, Gawler S, Kittel G, Menard S, Comer P, Muldavin E, Schafale M, Foti T, Josse C, Christy J (2008) Ecological Performance Standardsfor Wetland Mitigation: An Approach Based on Ecological IntegrityAssessments. NatureServe, Arlington, VA.