####### This data was collected and analyzed through a joint collaboration between the University of Delaware and Delaware State University, and funded by Lenfest Ocean's Program. ####### ####### Contacts: Danielle Haulsee, University of Delaware, Principal Investigator, dhaulsee@udel.edu Matthew Breece, University of Delaware, Principal Investigator, mwbreece@udel.edu Matthew Oliver, University of Delaware, Principal Investigator, moliver@udel.edu Dewayne Fox, Delaware State University, Principal Investigator, dfox@desu.edu ####### ####### Project summary: Species locations obtained from acoustic telemetry are rarely related to the dynamic ocean conditions the marine animals were experiencing at receiver locations. Satellite-measured sea surface conditions from the MODIS-Aqua platform provide measurements of oceanographic conditions on time (daily) and space (1 km^2) scales relevant to acoustic telemetry studies. Here we provide a dataset of matched species occurrence data, along with the concurrent static (bathymetry) and dynamic (remotely sensed sea surface conditions) environmental variables, for approximately 5 years (2009-2014) of acoustic telemetry data in the Delaware Bay and nearby coastal ocean. Our dataset focuses on two species of interest, the Sand Tiger (Carcharias taurus) and the Atlantic Sturgeon (Acipenser oxyrhynchus oxyrhynchus). ####### ####### Data description: This file contains daily-binned acoustic detections of Atlantic Sturgeon (Acipenser oxyrhynchus oxyrhynchus) and Sand Tigers (Charcharias taurus), from VEMCO acoustic receivers (VR2 and VR2Ws) moored within the Delaware Bay and Delaware Coastal Ocean, USA. The dataset includes the acoustic transmitter ID for each individual, as well as associated metadata (fork length (cm), total length (cm), sex, weight (kg)). Detection locations were matched to daily aggregated remote sensing environmental variables measured by the MODIS Aqua satellite sensor and can be found here: http://basin.ceoe.udel.edu/thredds/catalog.html?dataset=AquaClimatology1Day. ####### ####### Remote sensing environmental data definitions: Variable, Units, Description Depth, m, Water Depth chl_oc3, mg m-3, Chlorophyll Concentration, OC3 Algorithm a_412_qaa, m-1, Total absorption at 412 nm, QAA algorithm a_443_qaa, m-1, Total absorption at 443 nm, QAA algorithm a_469_qaa, m-1, Total absorption at 469 nm, QAA algorithm a_488_qaa, m-1, Total absorption at 488 nm, QAA algorithm a_531_qaa, m-1, Total absorption at 531 nm, QAA algorithm a_547_qaa, m-1, Total absorption at 547 nm, QAA algorithm a_555_qaa, m-1, Total absorption at 555 nm, QAA algorithm a_645_qaa, m-1, Total absorption at 645 nm, QAA algorithm a_667_qaa, m-1, Total absorption at 667 nm, QAA algorithm a_678_qaa, m-1, Total absorption at 678 nm, QAA algorithm bb_547_qaa, m-1, Total backscattering at 547 nm, QAA algorithm aph_443_qaa, m-1, Absorption due to phytoplankton at 443 nm, QAA algorithm adg_412_qaa, m-1, Absorption due to gelbstoff and detrital material at 412 nm, QAA algorithm c_547_qaa, m-1, Beam attenuation at 547 nm, QAA algorithm rrs_412, sr-1, Remote sensing reflectance at 412 nm rrs_443, sr-1, Remote sensing reflectance at 443 nm rrs_469, sr-1, Remote sensing reflectance at 469 nm rrs_488, sr-1, Remote sensing reflectance at 488 nm rrs_531, sr-1, Remote sensing reflectance at 531 nm rrs_547, sr-1, Remote sensing reflectance at 547 nm rrs_555, sr-1, Remote sensing reflectance at 555 nm rrs_645, sr-1, Remote sensing reflectance at 645 nm rrs_667, sr-1, Remote sensing reflectance at 667 nm rrs_678, sr-1, Remote sensing reflectance at 678 nm rrs_748, sr-1, Remote sensing reflectance at 748 nm rrs_859, sr-1, Remote sensing reflectance at 859 nm pic, mg m-3, Calcite Concentration, Balch and Gordon poc, mg m-3, Particulate Organic Carbon, D. Stramski, 2007 (443/555 version) SST, C, Sea Surface Temperature red_ch, unitless, Red Channel blue_ch, unitless, blue Channel green_ch, unitless, green Channel M_WK, unitless, Water Mass Classifications using Wards and Kmeans clustering after Oliver and Irwin 2008 M_WK_G, unitless, Gradient Strengths Across Water Mass Classifications using Wards and Kmeans clustering after Oliver and Irwin 2008