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description: Bitterroot Restoration, Inc. (BRI) contracted with the U.S. Fish and Wildlife Service atBenton Lake National Wildlife Refuge (BLNWR) to conduct an inventory of existing wetland vegetation and habitat, and to create a map of vegetation types on the refuge. This report is on the assessment and results of the work conducted by BRI. The field data collection was completed during the period from August 14 to October 1, 2001. The objective of this study is to characterize and quantify the wetland vegetation present on the BLNWR in terms of individual species, as well as vegetation types (habitat types and community types). This will be related spatially on a map created to present the vegetation data at a scale to show individual landform and vegetational features, such as small nesting islands and patches of bulrush. The project was designed for quantifying vegetative habitat values and liabilities, as well as for analyzing potential responses to various water management alternatives.; abstract: Bitterroot Restoration, Inc. (BRI) contracted with the U.S. Fish and Wildlife Service atBenton Lake National Wildlife Refuge (BLNWR) to conduct an inventory of existing wetland vegetation and habitat, and to create a map of vegetation types on the refuge. This report is on the assessment and results of the work conducted by BRI. The field data collection was completed during the period from August 14 to October 1, 2001. The objective of this study is to characterize and quantify the wetland vegetation present on the BLNWR in terms of individual species, as well as vegetation types (habitat types and community types). This will be related spatially on a map created to present the vegetation data at a scale to show individual landform and vegetational features, such as small nesting islands and patches of bulrush. The project was designed for quantifying vegetative habitat values and liabilities, as well as for analyzing potential responses to various water management alternatives.

This dataset delineates wetland ponds and emergent wetland vegetation in Mt. Rainier National Park. It was created through object based image analysis of high resolution imagery from 2006 and 2009 and LiDAR data acquired in fall of 2008. Riparian wetlands are not included in this dataset. Accuracy is only verified for wetland ponds in the subalpine region. Forested wetlands, riparian wetlands, and emergent vegetation were only visually assessed. This data maps all wetland habitat, but was primarily used to locate and delineate amphibian habitat in Mt. Rainier National Park.

These data represent Montana's wetland and riparian mapping status by 1:24,000 USGS quad. For quads within Montana that do not have updated mapping completed or modern mapping completed please download the NWI Legacy (outdated mapping) data from https://www.fws.gov/wetlands/Data/Data_Download.html.

This layer quantifies the yearly net carbon sequestration in Maryland's forests and wetlands.Carbon dioxide (CO2) is a naturally occurring greenhouse gas (GHG) found in the Earth’s atmosphere which plays a critical role in maintaining a climate suitable for life on this planet. Though beneficial to life, rising atmospheric concentrations of CO2 over the past century have been linked to increases in climate variability and change at local, regional, and global scales. Over the past 30 years, climate researchers have worked to quantify the flux of carbon between sources and sinks in the carbon cycle. Forested areas have been identified as one of the major carbon sinks existing on Earth. During the process of photosynthesis, trees remove CO2 from the atmosphere, releasing oxygen (O2) and converting carbon (C) to long term storage within the woody biomass of their trunks. Thus, the world’s forests hold an immense amount of carbon in standing trees, and have the potential to continue sequestering carbon as they grow. Wetlands also have a large capacity for sequestering carbon, particularly coastal wetlands which have high primary production and produce less methane (a gas which contributes to warming), than freshwater wetlands. Net sequestration values in this layer reflect both carbon sequestration and methane emissions.

This data layer was created as part of the Maryland Department of Natural Resources "Accounting for Maryland's Ecosystem Services" program.This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Map Service Link: https://mdgeodata.md.gov/imap/rest/services/Environment/MD_EcosystemServices/MapServer/12Download the Ecosystem Services layers at: https://www.dropbox.com/s/e6ovfcc01dxvnmo/EcosystemServices.gdb.zip?dl=0

Idaho’s landscape-scale wetland condition assessment tool— Methods and applications in conservation and restoration planningLandscape-scale wetland threat and impairment assessment has been widely applied, both at the national level (NatureServe 2009) and in various states, including Colorado (Lemly et al. 2011), Delaware and Maryland (Tiner 2002 and 2005; Weller et al. 2007), Minnesota (Sands 2002), Montana (Daumiller 2003, Vance 2009), North Dakota (Mita et al. 2007), Ohio (Fennessy et al. 2007), Pennsylvania (Brooks et al. 2002 and 2004; Hychka et al. 2007; Wardrop et al. 2007), and South Dakota (Troelstrup and Stueven 2007). Most of these landscape-scale analyses use a relatively similar list of spatial layer inputs to calculate metrics for condition analyses. This is a cost-effective, objective way to obtain this information from all wetlands in a broad geographic area. Similar landscape-scale assessment projects in Idaho (Murphy and Schmidt 2010) used spatial analysis to estimate the relative condition of wetlands habitats throughout Idaho. Spatial data sources: Murphy and Schmidt (2010) reviewed literature and availability of spatial data to choose which spatial layers to include in their model of landscape integrity. Spatial layers preferably had statewide coverage for inclusion in the analysis. Nearly all spatial layers were downloaded from the statewide geospatial data clearinghouse, the Interactive Numeric and Spatial Information Data Engine for Idaho (INSIDE Idaho; http://inside.uidaho.edu/index.html). A complete list of layers used in the landscape integrity model is in Table 1. Statewide spatial layers were lacking for some important potential condition indicators, such as mine tailings, beaver presence, herbicide or pesticide use, non-native species abundance, nutrient loading, off-highway vehicle use, recreational and boating impacts, and sediment accumulation. Statewide spatial layers were also lacking for two presumably important potential indicators of wetland/riparian condition, recent timber harvest and livestock grazing. To rectify this, GIS models of potential recent timber harvest and livestock grazing were created using National Land Cover Data, grazing allotment maps, and NW ReGAP land cover maps. Calculation of landscape and disturbance metrics: We used a landscape integrity model approach similar to that used by Lemly et al. (2011), Vance (2009), and Faber-Langendoen et al. (2006). Spatial analysis in GIS was used to calculate human land use, or disturbance, metrics for every 30 m2 pixel across Idaho. A single raster layer that indicated threats and impairments for that pixel was produced. This was accomplished by first calculating the distance from each human land use category, development type, or disturbance for each pixel. This inverse weighted distance model is based on the assumption that ecological condition will be poorer in areas of the landscape with the most cumulative human activities and disturbances. Condition improves as you move toward least developed areas (Faber-Langendoen et al. 2006, Vance 2009, Lemly et al. 2011). Land uses or disturbances within 50 m were considered to have twice the impact of those 50 - 100 m away. For this model, land uses and disturbances > 100 m away were assumed to have zero or negligible impact. Because not all land uses impact wetlands the same way, weights for each land use or disturbance type were then determined using published literature (Hauer et al. 2002, Brown and Vivas 2005, Fennessy et al. 2007, Durkalec et al. 2009). A list of weights applied to each land use or disturbance type is in Table 2. A condition value for each pixel was then calculated. For example, the value for a pixel with a 2-lane highway and railroad within 50 m and a home and urban park between 50 and 100 m would be: Weight x Distance = Impact Factor2-lane highway = 7.81 2 15.62railroad = 7.81 2 + 15.62single family home - low density = 6.91 1 + 6.91recreation / open space - medium intensity = 4.38 1 + 4.38 Total Disturbance Value = 42.53The integrity of each pixel was then ranked relative to all others in Idhao using methods analogous to Stoddard et al. (2005), Fennessy et al. (2007), Mita et al. (2007), and Troelstrup and Stueven (2007). Five condition categories based on the sum of weighted impacts present in each pixel were used: 1 = minimally disturbed (top 1% of wetlands); wetland present in the absence or near absence of human disturbances; zero to few stressors are present; land use is almost completely not human-created; equivalent to reference condition; conservation priority;2 = lightly disturbed (2 - 5%); wetland deviates the least from that in the minimally disturbed class based on existing landscape impacts; few stressors are present; majority of land use is not human-created; these are the best wetlands in areas where human influences are present; ecosystem processes and functions are within natural ranges of variation found in the reference condition, but threats exist; conservation and/or restoration priority; 3 = moderately disturbed (6 - 15%); several stressors are present; land use is roughly split between human-created and non-human land use; ecosystem processes and functions are impaired and somewhat outside the range of variation found in the reference condition, but are still present; ecosystem processes are restorable;4 = severely disturbed (16 - 40%); numerous stressors are present; land use is majority human-created; ecosystem processes and functions are severely altered or disrupted and outside the range of variation found in the reference condition; ecosystem processes are restorable, but may require large investments of energy and money for successful restoration; 5 = completely disturbed (bottom 41 - 100%); many stressors are present; land use is nearly completely human-created; ecosystem processes and functions are disrupted and outside the range of variation in the reference condition; ecosystem processes are very difficult to restore.The resulting layer was then filtered using the map of potential wetland occurrence to show only those pixels potentially supporting wetlands.Results of GIS landscape-scale assessment were verified by comparing results with the condition of wetlands determined by in the field using rapid assessment methods. The landscape assessment matched the rapidly assessed condition estimated in the field 61% of the time (Murphy et al. 2012). Thirty-one percent of the sites were misclassified by one condition class and 8% misclassified by two condition classes. These results were similar to an accuracy assessment of landscape scale assessment performed by Mita et al. (2007) in North Dakota. When sites classified correctly and those only off by one condition class were combined (92% of the samples), results were similar to Vance (2009) in Montana (85%). The model of landscape integrity performed much better than the initial prototype model produced for Idaho by Murphy and Schmidt (2010).

Report: Measuring outcomes of wetland restoration, enhancement, and creation in Idaho—Assessing potential functions, values, and condition in a watershed context This dataset is a compilation of results of various wetland assessment projects conducted in Idaho since 2006. It includes spatial and tabular information from 211 wetland assessment areas, of which 64 were rapidly assessed for function, ecosystem services, and condition (primarily in restored, enhanced, and created wetlands), and 147 rapidly assessed for condition only.The Wetland Ecosystem Services Protocol for the United States (WESPUS) (Adamus 2011) was used to rapidly assess the potential hydrologic, water quality, carbon sequestration, and habitat (fish, aquatic, terrestrial) functions and ecosystem services of 51 wetlands. This method also addresses wetland stressors and integrity. It is logic-based, incorporating wetland ecologic principles of hydrology, biogeochemistry, ecology, and hydrogeomorphic (HGM) assessment. It is transparent, providing explanations of the assumptions and algorithms used in models that score wetland functions and ecosystem services for a site. The models incorporate 140 indicators observable during the field assessment. WESPUS estimates the value of a wetland function (ecosystem service) according to the opportunity and relative importance that a particular wetland has in providing that function. This rapid (Level II) method that is repeatable and relatively easily applied by wetland specialists and field ecologists (Adamus 2011). A limited number of wetlands were rapidly assessed for function and condition using alternative methods explained in the cited documents (13 wetlands using Wetland Rating System for Eastern Washington (Hruby 2004); 6 wetlands usiong Montana Department of Transportation Wetland Assessment Method (Berglund and McEldowney 2008). The condition of was rapidly assessed using the “Idaho Wetland Condition Rapid Assessment Method” (Idaho RAM) (described in Murphy and Schmidt 2010). This method was modeled after similar land use and stressor checklists. Idaho RAM is based on field observation of disturbance and stress indicators. It consists of both land use and stressor checklists. The Land-use Checklist is applied by estimating the percent of the assessment area and percent of the 100 m buffer occupied by each land-use on the checklist. The Stressor Checklist is applied by marking the presence of each stressor on the checklist that is observed in the AA and in the surrounding 50 m wide buffer. High stressor and human land use scores mean that a greater number of indicators of stress and impairments to wetland condition and integrity were observed. The condition of vernal pools and playas was assessed using a modified Idaho RAM focused on indicators specific to these unique wetland habitats.