Echolocation detections and digital video surveys provide reliable estimates of the relative density of harbour porpoises Laura D. Williamson 1,a Kate L. Brookes 2 Beth E. Scott 1 Isla M. Graham 3 Gareth Bradbury 4 Philip S. Hammond 5 Paul M. Thompson 3 1 Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 2TZ, UK 2 Marine Scotland Science, Marine Laboratory, 375 Victoria Road, Aberdeen, AB11 9DB, UK 3 Institute of Biological and Environmental Sciences, Lighthouse Field Station, University of Aberdeen, Cromarty, IV11 8YJ, UK 4 WWT Consulting, Slimbridge, Gloucestershire, GL2 7BT, UK 5 Sea Mammal Research Unit, Scottish Oceans Institute, University of St. Andrews, Fife, KY16 8LB, UK a e-mail: l.williamson@abdn.ac.uk ###################################################### Visual_Survey_Data.rds R data file of the visual survey data in distance format. To open the data, use the following code in R: visual_data<-readRDS("file path...\\Visual_Survey_Data.rds") str(visual_data) obsdata<-visual_data$obsdata preddata<-visual_data$preddata segdata<-visual_data$segdata distdata<-visual_data$distdata survey.area<-visual_data$survey.area variables included: depth = depth in metres coastdist = distance to coast in metres slope = slope psndgrvsnd = percent of sediment that is sand or gravelly sand Transect.Label Sample.Label Effort = in km surveyed latitude = in WGS84 longitude = in WGS84 x = longitude in UTM30N y = latitude in UTM30N julianday = Julian day obsname = name of observer distance = perpendicular distance to harbour porpoies in metres size = cluster size of porpoise seastate = on Beaufort scale sightingco = subjective measure of sighting conditions glare = amount of glare on sea surface cloud = amount of cloud cover precipitat = amount of precipitation observer = using single-observer MCDS to estimate detection function therefore observer is 1 for all observations detected = using single-observer MCDS to estimate detection function therefore detected is 1 for all observations object = ID of harbour porpoise sighting width = width of prediction grid cell in metres height = height of prediction grid cell in metres See Appendix S1 for code showing how to perform DSM. ###################################################### Digital_Survey_Data.rds R data file of the digital survey data in distance format. To open the data, use the following code in R: Digital_data<-readRDS("file path...\\Digital_Survey_Data.rds") str(Digital_data) obsdata<-Digital_data$obsdata preddata<-Digital_data$preddata segdata<-Digital_data$segdata survey.area<-Digital_data$survey.area variables included: depth = depth in metres coastdist = distance to coast in metres slope = slope psndgrvsnd = percent of sediment that is sand or gravelly sand Transect.Label Sample.Label Effort = in km surveyed latitude = in WGS84 longitude = in WGS84 x = longitude in UTM30N y = latitude in UTM30N size = cluster size of porpoise object = ID of harbour porpoise sighting width = width of prediction grid cell in metres height = height of prediction grid cell in metres Strip_Width = width of strip covered by camera - need to divide by 1/2 when inputting into DSM See Appendix S1 for code showing how to perform DSM. ###################################################### Visual_vs_Digital_Comparison.xlsx Data used for bootstrap comparison of visual and digital DSM estimates to calculate the scaling factor for digital surveys. variables included: Digital_Mean = mean density estimated in each cell in DSM of digital data Digital_SD = standard deviation of each cell estimated from DSM of digital data Visual_Mean = mean density estimated in each cell in DSM of visualdata Visual_SD = standard deviation of each cell estimated from DSM of visualdata See Appendix S2 for code showing how to estimate scaling factor as a proxy for detection probability of digital surveys. ###################################################### Acoustic_Data.xlsx Data used for comparison of acoustic indices to estimates of relative density from DSM of visual survey data. Variables included: Location = ID of C-POD location Mean_WT = mean waiting time between detections SD_WT = standard deviation of waiting time between detections Mean_DMP/D = mean detection positive minutes per day SD_DPM/D = standard deviation of detection positive minutes per day MEAN_DPM/H = mean detection positive minutes per hour SD_DPM/H = standard deviation of detection positive minutes per hour Mean_DPI10 = mean number of detection positive 10 minute intervals in a day SD_DPI10 = standard deviation of detection positive 10 minute intervals Mean_DPI20 = mean number of detection positive 20 minute intervals in a day SD_DPI20 = standard deviation of detection positive 20 minute intervals Mean_DPI30 = mean number of detection positive 30 minute intervals in a day SD_DPI30 = standard deviation of detection positive 30 minute intervals Mean_DPI40 = mean number of detection positive 40 minute intervals in a day SD_DPI40 = standard deviation of detection positive 40 minute intervals Mean_DPI50 = mean number of detection positive 50 minute intervals in a day SD_DPI50 = standard deviation of detection positive 50 minute intervals Mean_DPI60 = mean number of detection positive 60 minute intervals in a day SD_DPI60 = standard deviation of detection positive 60 minute intervals Mean_DPI70 = mean number of detection positive 70 minute intervals in a day SD_DPI70 = standard deviation of detection positive 70 minute intervals Mean_DPI80 = mean number of detection positive 80 minute intervals in a day SD_DPI80 = standard deviation of detection positive 80 minute intervals Mean_DPI90 = mean number of detection positive 90 minute intervals in a day SD_DPI90 = standard deviation of detection positive 90 minute intervals Mean_Visual = mean relative density of cells within a 1 km radius of C-POD site SD_Visual = standard deviation of relative density estimates within a 1 km radius of C-POD site Spearman’s rho used to determine correlation between visual and acoustic metrics.