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Monitoring waterfowl numbers during primary migration and winter periods is essential to developing effective habitat conservation and management strategies. The relationship between available habitat and waterfowl numbers obtained from aerial survey transects is not well studied. To determine these relationships, multispectral SPOT-5 satellite imagery acquired for Sequoyah National Wildlife Refuge close to the time of waterfowl surveys was used to map habitat conditions. Robust Random Forest classification trees were used to model 16 land cover types using 416 reference locations collected in the field or derived from aerial photos close to or during waterfowl survey dates. The normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and a simple ratio (SR) of red and near infrared bands were used to enhance classification accuracy for key habitat areas and abundance of water. Terrain variables such as slope, solar illumination and cosine transformed aspect derived from a digital elevation model (DEM) were also used to enhance habitat classification. Random Forest (RF) models were also compared to support vector machines (SVM) and cforest (CF) conditional inference trees. We used error matrices and the Kappa agreement statistic (K) to compare model results from each classifier. Results indicated that a tuned RF classifier showed better performance (K=0.73) than SVM (K=0.65) and unbiased cforest trees (K=0.63). Overall class agreement between similar RF and cforest models, designed to reduce predictor variable selection bias, was also relatively low (K=0.47). A final tuned RF model was selected resulting in 75% accuracy overall and was used to map habitat types for the refuge and surrounding landscape. We found that elevation and minimum noise fraction (MNF) bands were the most important predictor variables. MNF bands can help to reduce the number of correlated variables entering into a classification model when a larger number of correlated spectral bands are used. Similar forest types such as riverine, bottomland hardwood, and floodplain forest showed the greatest misclassification error. Overall, the RF model and SPOT-5 leaf-off imagery generated accurate land cover data for assessing habitat conditions during waterfowl surveys

A primary focus of Sequoyah National Wildlife Refuge (SNWR) is to provide habitat and foraging opportunities for waterfowl and other wetland dependent species. SNWR currently meets this objective by providing wetland and agricultural habitat. Wetland restoration and enhancement projects have occurred in recent years, with the intent to sustain healthier and more diverse waterfowl and wildlife populations. However, the relationship between habitat and food availability relative to waterfowl presence has yet to be quantified. This information is necessary to determine how SNWR can best manage refuge habitats for wintering waterfowl populations to ensure the available foraging resources on the refuge or within the surrounding landscape are sufficient to meet the energetic demands of waterfowl populations. Specifically, our goal was determine current carrying capacity for wintering waterfowl provided by SNWR relative to the surrounding landscape within the foraging flight distance of focal species: mallard, gadwall, and snow geese. Models included in this study emphasized efficient land use and maximization of resources within the SNWR refuge boundary in consideration of food resources available with the FFD of wintering waterfowl (e.g. Johnson et al. 2014). Specifically, we calculated the current energetic carrying capacity within Sequoyah and the surrounding FFD to determine to contributions of on-refuge agriculture relative to native wetland habitat types. We then determined the energetic effects of reductions in the area farmed on the refuge, changes to crop type and proportion (i.e., green browse versus standing grain) planted on the refuge, changes to the timing and proportion of standing crops mowed during the winter period, and hypothetical reductions in crop yield that could result from implementation of National Wildlife Refuge System policy eliminating the use of neonicotinoid coated and/or genetically modified seeds. To offset energetic losses associated with reductions or changes to on-refuge agricultural production, we determined the energetic gains associated with the creation of one passive and one actively managed wetland. Our model outcomes can be used to guide land management and restoration decisions and be incorporated into Habitat Management and Comprehensive Conservation Plans.

Lymphangioma circumscriptum is a superficially localized variant of lymphangioma. The characteristic clinical presentation is a “frogspawn” grouping of vesicles or papulovesicles on the proximal limb or limb girdle areas. Though most lymphangiomas develop congenitally, the lymphangioma circumscriptum subtype is known to present in adults. We report a case of lymphangioma circumscriptum on the left inframammary area of an African American female with an unusual supernumerary nipple-like clinical presentation. Our patient presented with a firm, smooth, hypopigmented papule, and the clinical diagnosis of keloid was made initially. However, she returned reporting growth of the lesion and was noted to have a firm, exophytic, lobulated, pink to skin-colored nodule. Histopathological examination demonstrated dilated lymphatic vessels, consistent with the diagnosis of lymphangioma. The presentation as a firm, hypopigmented papule and later exophytic, lobulated, skin-colored nodule in our case represents a clinical presentation of lymphangioma circumscriptum not previously described in the literature. Correct diagnosis in lymphangioma circumscriptum is vital, as recurrence following surgical resection and secondary development of lymphangiosarcoma and squamous cell carcinoma following treatment with radiation have been reported. Thus, it is important to consider lymphangioma circumscriptum in the differential of similar lesions in the future to allow appropriate diagnosis, treatment, and monitoring.

In 2012, we evaluated aerial transects (strip transects) for surveying wintering waterfowl at Sequoyah NWR and the landscape immediately surrounding the refuge. In surveying the landscape around the refuge, we attempted to capture the area used by waterfowl making daily commute flights to forage off refuge (Johnson et al. 2014). In total, we surveyed 26,895 hectares, with the 8,464.8 hectare refuge comprising 31% of the survey area. The survey consisted of 33 transects that ranged in length from 1.3 km to 12.0 km (Figure 1); on-refuge survey consisted of 26 transects that ranged in length from 0.6 km to 7.3 km. Our purpose was to examine the feasibility of aerial surveys and to develop waterfowl density and population estimates for local biological planning efforts, such as developing population estimates linked to energetic carrying capacity models (Reinecke and Loesch 1996, Michot 1997).Between February 2012 and February 2014, eleven surveys were conducted; two in the 2011-2012 non-breeding period, five in the 2012-2013 non-breeding period and 4 in the 2013-2014 non-breeding period. Due to low numbers of waterfowl observations off-refuge, we discontinued off-refuge surveys in the 2013-2014 winter period. Flight procedures (height above ground, air speed, etc.) followed the Standard Operating Procedures (SOP; Schmidt et al. 2014), and attempts were made to identify waterfowl observed to species (Bowman 2014).Peak on-refuge population estimates varied by guild and year (Tables 2A-E). Peak estimates were 31,871.7 ± 13,085.7 (SE) for Mallards in mid-January 2013; 3,848.6 ± 1,206.5 (SE) for Gadwall in late-November 2012; 3,722.7 ± 2,101.3 (SE) for all other species of dabbling ducks combined but excluding Mallards and Gadwall in February 2012; 314.7 ± 132.5 (SE) for all species of diving ducks combined in January 2014; and 32,935.1 ± 13,049.9 (SE) for Lesser Snow Geese and Canada Geese combined in mid-January 2013. Precision for individual on-refuge survey estimates was generally poor (CV > 20%). We had >1 on-refuge survey per month in all years combined for the months of December (N = 2), January (N = 4), and February (N = 3). The mean on-refuge population estimate for Mallards was 11,200 ± 3,626 (SE) in December, 18,266 ± 2,326 (SE) in January, and 18,678 ± 3,177 (SE) in February. For the same months respectively, mean population estimates for Gadwall were 1,468 ± 452 (SE), 1,034 ± 105 (SE), and 577 ± 115 (SE) and for Lesser Snow Geese and Canada Geese combined were 17,486 ± 5,507 (SE), 23,131 ± 2,454 (SE) and 15,177 ± 3,134 (SE). The precision of average monthly on-refuge estimates was considerably better than for individual survey periods. Precision for average on-refuge estimates for November and January were < 20% for Mallards, Gadwall and Geese; precision of average on-refuge estimates for December was around 30% for Mallards, Gadwall, and Geese (Table 3). Despite insufficient precision for individual survey estimates, average monthly on-refuge population estimates had suitable precision (CVs 1 year should be used if suitably precise (i.e., CV