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Layer: Northern Gannet - Winter, Relative Abundance Model (ID: 58)

Name: Northern Gannet - Winter, Relative Abundance Model

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Type: Raster Layer

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Description: This dataset represents relative seabird abundance predictions from spatial models developed for the New York offshore spatial planning area. Raster values represent the sum of the predicted relative abundance (individuals sighted per km per 15 minutes) for each modeled species across all seasons in which they were modeled. Abundance was treated as zero for all seasons in which a species or group was not modeled.DETAILED METHODS. Seabird sightings data for the offshore planning region were extracted by NOAA NCCOS from the Manomet Bird Observatory’s (MBO, now the Manomet Center for Conservation Sciences, or MCCS) Cetacean and Seabird Assessment Program (CSAP) database, which contains over 9,000 survey locations. During these surveys a small number of expert observers were placed on research vessels undertaking a wide variety of work. Seabirds were identified to the most specific taxonomic level possible, usually species, and counted within a fixed strip width of 300 m at one side of a ship as it traveled on a straight course, at a constant speed (generally 8-12 knots). Observations were separated by season, and for each species or group sighting record in each season, the number of individuals of that species observed during the timed survey was divided by the corresponding survey tract area to yield an index of relative abundance that was standardized by both time and area, resulting in SPUE represented as sightings per 15 minutes per sq. km of transect footprint. Based on available high-resolution data coverage within the offshore planning area and previous studies of environmental correlates of seabird distribution and abundance, NOAA NCCOS identified 11 potential environmental predictor variables. These variables were: bottom depth; bottom slope; slope-of-slope; distance from shore; signed distance from shelf; mean sediment grain size; water-column stratification; sea surface temperature; surface turbidity measure; surface chlorophyll-a concentration; and zooplankton biomass. For each season with sufficient data within each species selected for predictive modeling, NOAA NCCOS modeled the transect estimates of SPUE as point samples (located at the centroid of each transect) of two spatial random processes, Stage I and Stage II. Stage I used binary (presence/absence) data from the CSAP surveys and Stage II used relative abundance (i.e., SPUE) observations for each species or group from the same surveys, but did not consider locations where SPUE=0. Within each stage of the model, they used a regression-Kriging framework to account for both seabird-environment relationships and spatial structure. Both Stage I and Stage II models included two components: a trend model that used a generalized linear model (GLM) and incorporated environmental predictors and a geostatistical model that accounted for spatial autocorrelation in the residuals. NCCOS assessed model performance and error via cross-validation, producing numerous statistics for model evaluation.The data used to develop these models do not capture many dynamic aspects of seabird ecology and were collected in the 1980s. Modeling required an assumption that the climatological patterns of ocean conditions have not undergone substantial shifts since then. Finally, survey biases (e.g., detectability) are likely to vary between species. These issues underscore the importance of treating the measures of relative abundance presented here as proxies for underlying patterns. Nonetheless, these maps represent the first high-resolution depiction of spatial patterns in the marine avifauna of New York.

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Copyright Text: CCMA credits these people for deriving this dataset: Menza, C., B.P. Kinlan, D.S. Dorfman, M. Poti and C. Caldow

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