Supplementary environmental point data: "A climate-associated multi-species cryptic genetic cline in the northwest Atlantic"

• Main Author: Stanley, Ryan
• Format: Dataset
• Language: unknown
• Published: figshare 2017
• Subjects: 60306 Evolutionary Impacts of Climate Change; FOS Biological sciences; Marine Biology; 60411 Population, Ecological and Evolutionary Genetics; Canada; Northwest Atlantic
• Online Access: https://dx.doi.org/10.6084/m9.figshare.5151907; https://figshare.com/articles/dataset/Supplementary_environmental_point_data_A_climate-associated_multi-species_cryptic_genetic_cline_in_the_northwest_Atlantic_/5151907

This is supplementary environmental data used in environmental population structure analyses: "A climate-associated multi-species cryptic genetic cline in the northwest Atlantic" This data is described in the methods as: Temperature and salinity data were aggregated to seasonal climatological data (averaged across 2002-2012) layers, effectively corresponding with winter (January – March), spring (April – June), summer (July – September), and fall (October – December). Seasonal sea surface temperature (SST) and sea surface salinity (SSS) were assembled at spatial resolutions interpretable to 1 km 2 from Level 3 SST climatological satellite data, including Advanced Very High Resolution Radiometer data (AVHRR Atlantic; compiled by Fisheries and Oceans Canada) and global oceanographic climatological SSS composites (Tyberghein et al. 2012). Benthic temperature and benthic salinity climatological data layers were assembled at spatial resolutions interpretable to 8 km 2 from a numerical climatological model (GLORYS2V1) adapted to the study area by Fisheries and Oceans Canada. We represented topographic complexity of the seafloor (interpretable to 1 km 2 ) by east–west and north–south components of aspect, slope, plan and profile curvature, and rugosity (Sbrocco & Barber 2013). These data layers were used as predictive surfaces for each of the five species native to the range (Table 1). From these data layers, we extracted point estimates of each environmental variable for each genetic sample location to evaluate genetic-environmental relationships. This dataset represents these point estimates.