Layer: Wetland Condition (ID: 1)

Name: Wetland Condition

Display Field: VALUE

Type: Raster Layer

Geometry Type: null

Description: Landscape-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 Factor 2-lane highway = 7.81 2 15.62 railroad = 7.81 2+15.62 single family home - low density = 6.91 1+6.91 recreation / open space - medium intensity = 4.38 1+4.38 Total Disturbance Value = 42.53 The 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).

Copyright Text: This dataset was created by Idaho Fish and Game.

Default Visibility: false

MaxRecordCount: 0

Supported Query Formats: JSON, geoJSON, PBF

Min Scale: 0

Max Scale: 0

Supports Advanced Queries: false

Supports Statistics: false

Has Labels: false

Can Modify Layer: false

Can Scale Symbols: false

Use Standardized Queries: true

Supports Datum Transformation: true

Extent:

Drawing Info:

N/A Advanced Query Capabilities:

HasZ: false

HasM: false

Has Attachments: false

HTML Popup Type: esriServerHTMLPopupTypeNone

Type ID Field: null

Fields:

OBJECTID ( type: esriFieldTypeOID, alias: OBJECTID )
VALUE ( type: esriFieldTypeInteger, alias: VALUE )
COUNT_ ( type: esriFieldTypeInteger, alias: COUNT_ )
Label ( type: esriFieldTypeString, alias: Label, length: 23 )

Supported Operations: Query Generate Renderer Return Updates

Iteminfo Thumbnail Metadata