Gosling body mass can affect first year survival, recruitment, adult body size, and future fecundity of geese, and can serve as an indicator of forage availability and quality on brood-rearing areas. From 2012-2014 we measured body mass of 76 black brant (Branta bernicla nigricans) and 268 lesser snow goose (Chen caerulescens caerulescens) goslings of known age on the Colville River Delta (CRD) of northern Alaska to determine if there was evidence of density-dependent declines in gosling growth following recent population increases of those species and sympatric greater white-fronted geese (Anser albifrons frontalis). We contrasted contemporary body mass of brant goslings and forage biomass in brood-rearing habitats that were shared by all species, with measures obtained on, and near the CRD in the 1990s, prior to the establishment of snow goose nesting colonies in the area. Body mass of brant goslings recaptured between 25 and 32 days of age had not changed over the past 2 decades, despite an influx of snow geese, and increases in populations of brant and white-fronted geese. At 30 days of age, body mass of brant goslings on the CRD was 100-400 g heavier than for brant goslings of the same age on the Yukon-Kuskokwim Delta (YKD), Alaska. Contemporary biomass of grazed Carex subspathacea on CRD brood-rearing areas was comparable to the 1990s and was 2-4 times greater than for the same plant community on the YKD. Historical data on growth of snow goose goslings were not available for the CRD. However, average body mass of 34-day-old snow goose goslings was >230 g heavier than for conspecifics of the same age in the Hudson Bay region. We conclude that the establishment of nesting snow geese on the CRD has not negatively affected brant gosling growth, and that recent population increases of all species have likely not been constrained by forage availability on brood-rearing areas. Barring demographic changes elsewhere in their annual cycles, we predict that goose populations will continue to increase in northern Alaska. However, snow geese are increasing more rapidly than brant in the region. Because the black brant population has periodically been below conservation objectives, the effects of the increasing number of snow geese on forage biomass and growth of brant goslings in northern Alaska should be monitored. Ó 2017 The Wildlife Society.

Satellite and acoustic remote sensing enable the collection of high-resolution seafloor bathymetry data for integration with terrestrial elevations into coastal terrain models. A model of Tutuila Island, American Samoa, is created using depths derived from IKONOS satellite imagery to provide data in the near-shore gap between sea level and the beginning of sonar data at 10-15 m depth. A derivation method gauging the relative attenuation of blue and green spectral radiation is proven the most effective of several proposed in recent literature. The resulting coastal terrain model is shown to be accurate through statistical analyses and topographic profiles.

Tools that can monitor biomass and nutritional quality of forage plants are needed to understand how arctic herbivores may respond to the rapidly changing environment at high latitudes. The Normalized Difference Vegetation Index (NDVI) has been widely used to assess changes in abundance and distribution of terrestrial vegetative communities. However, the efficacy of NDVI to measure seasonal changes in biomass and nutritional quality of forage plants in the Arctic remains largely un-evaluated at landscape and fine-scale levels. We modeled the relationships between NDVI and seasonal changes in aboveground biomass and nitrogen concentration in halophytic graminoids, a key food source for arctic-nesting geese. The model was calibrated based on data collected at one site and validated using data from another site. Effects of spatial scale on model accuracy were determined by comparing model predictions between NDVI derived from moderate resolution (250 × 250 m pixels) satellite data and high resolution (20 cm diameter area) handheld spectrometer data. NDVI derived from the handheld spectrometer was a superior estimator (R 2 ≥ 0.67) of seasonal changes in aboveground biomass compared to satellite-derived NDVI (R 2 ≤ 0.40). The addition of temperature and precipitation variables to the model for biomass improved fit, but provided minor gains in predictive power beyond that of the NDVI-only model. This model, however, was only a moderately accurate estimator of biomass in an ecologically-similar halophytic graminoid wetland located 100 km away, indicating the necessity for site-specific validation. In contrast to assessments of biomass, satellite-derived NDVI was a better estimator for the timing of peak percent of nitrogen than NDVI derived from the handheld spectrometer. We confirmed that the date when NDVI reached 50% of its seasonal maximum was a reasonable approximation of the period of peak spring vegetative green-up and peak percent nitrogen. This study demonstrates the importance of matching the scale of NDVI measurements to the vegetation properties of biomass and nitrogen phenology.

Seagrass meadows, one of the world's most widespread and productive ecosystems, provide a wide range of services with real economic value. Worldwide declines in the distribution and abundance of seagrasses and increased threats to coastal ecosystems from climate change have prompted a need to acquire baseline data for monitoring and protecting these important habitats. We assessed the distribution and abundance of eelgrass (Zostera marina) along nearly 1200 km of shoreline on the lower Alaska Peninsula, a region of expansive eelgrass meadows whose status and trends are poorly understood. We demonstrate the effectiveness of a multi-scale approach by using Landsat satellite imagery to map the total areal extent of eelgrass while integrating field survey data to improve map accuracy and describe the physical and biological condition of the meadows. Innovative use of proven methods and processing tools was used to address challenges inherent to remote sensing in high latitude, coastal environments. Eelgrass was estimated to cover ~31,000 ha, 91% of submerged aquatic vegetation on the lower Alaska Peninsula, nearly doubling the known spatial extent of eelgrass in the region. OPEN ACCESSRemote Sens. 2014, 6 12448Mapping accuracy was 80%-90% for eelgrass distribution at locations containing adequate field survey data for error analysis.

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