DOI: 10.5061/dryad.ncjsxkt40
title: Behavioral responses of common dolphins to naval sonar

description: Despite strong interest in how noise affects marine mammals, little is
 known about the most abundant and commonly exposed taxa. Social delphinids
 occur in groups of hundreds of individuals that travel quickly, change
 behavior ephemerally, and are not amenable to conventional tagging
 methods, posing challenges in quantifying noise impacts. We integrated
 drone-based photogrammetry, strategically-placed acoustic recorders, and
 broad-scale visual observations to provide complimentary measurements of
 different aspects of behavior for short- and long-beaked common dolphins.
 We measured behavioral responses during controlled exposure experiments
 (CEEs) of military mid-frequency (3-4 kHz) active sonar (MFAS) using
 simulated and actual Navy sonar sources. We used latent-state Bayesian
 models to evaluate response probability and persistence in exposure and
 post-exposure phases. Changes in sub-group movement and aggregation
 parameters were commonly detected during different phases of MFAS CEEs but
 not control CEEs. Responses were more evident in short-beaked common
 dolphins (n=14 CEEs), and a direct relationship between response
 probability and received level was observed. Long-beaked common dolphins
 (n=20) showed less consistent responses, although contextual differences
 may have limited which movement responses could be detected. These are the
 first experimental behavioral response data for these abundant dolphins to
 directly inform impact assessments for military sonars.
description: We used complementary visual and acoustic sampling methods at
 variable spatial scales to measure different aspects of common dolphin
 behavior in known and controlled MFAS exposure and non-exposure contexts.
 Three fundamentally different data collection systems were used to sample
 group behavior. A broad-scale visual sampling of subgroup movement was
 conducted using theodolite tracking from shore-based stations. Assessments
 of whole-group and sub-group sizes, movement, and behavior were conducted
 at 2-minute intervals from shore-based and vessel platforms using
 high-powered binoculars and standardized sampling regimes. Aerial
 UAS-based photogrammetry quantified the movement of a single focal
 subgroup. The UAS consisted of a large (1.07 m diameter) custom-built
 octocopter drone launched and retrieved by hand from vessel platforms. The
 drone carried a vertically gimballed camera (at least 16MP) and sensors
 that allowed precise spatial positioning, allowing spatially explicit
 photogrammetry to infer movement speed and directionality. Remote-deployed
 (drifting) passive acoustic monitoring (PAM) sensors were strategically
 deployed around focal groups to examine both basic aspects of
 subspecies-specific common dolphin acoustic (whistling) behavior and
 potential group responses in whistling to MFAS on variable temporal scales
 (Casey et al., in press). This integration allowed us to evaluate
 potential changes in movement, social cohesion, and acoustic behavior and
 their covariance associated with the absence or occurrence of exposure to
 MFAS. The collective raw data set consists of several GB of continuous
 broadband acoustic data and hundreds of thousands of photogrammetry
 images.  Three sets of quantitative response variables
 were analyzed from the different data streams: directional persistence and
 variation in speed of the focal subgroup from UAS photogrammetry; group
 vocal activity (whistle counts) from passive acoustic records; and number
 of sub-groups within a larger group being tracked by the shore station
 overlook. We fit separate Bayesian hidden Markov models (HMMs) to each set
 of response data, with the HMM assumed to have two states: a baseline
 state and an enhanced state that was estimated in sequential 5-s blocks
 throughout each CEE. The number of subgroups was recorded during periodic
 observations every 2 minutes and assumed constant across time blocks
 between observations. The number of subgroups was treated as missing data
 30 seconds before each change was noted to introduce prior uncertainty
 about the precise timing of the change. For movement, two parameters
 relating to directional persistence and variation in speed were estimated
 by fitting a continuous time-correlated random walk model to spatially
 explicit photogrammetry data in the form of location tracks for focal
 individuals that were sequentially tracked throughout each CEE as a proxy
 for subgroup movement. Movement parameters were assumed
 to be normally distributed. Whistle counts were treated as normally
 distributed but truncated as positive because negative count data is not
 possible. Subgroup counts were assumed to be Poisson distributed as they
 were distinct, small values. In all cases, the response variable mean was
 modeled as a function of the HMM with a log link:
 log(<em>Response<sub>t</sub></em>)<em><sub> </sub></em>= l<sub>0</sub> + l<sub>1</sub><em>Z<sub> t</sub></em> where at each 5-s time block <em>t,</em> the hidden state<em> </em>took values of <em>Z<sub>t </sub></em>= 0 to identify one state with a baseline response level l<sub>0</sub>, or <em>Z<sub>t </sub></em>= 1 to identify an “enhanced” state, with l<sub>1 </sub>representing the enhancement of the quantitative value of the response variable. A flat uniform (-30,30) prior distribution was used for l<sub>0</sub> in each response model, and a uniform (0,30) prior distribution was adopted for each l<sub>1 </sub>to constrain enhancements to be positive. For whistle and subgroup counts, the enhanced state indicated increased vocal activity and more subgroups. A common indicator variable was estimated for the latent state for both the movement parameters, such that switching to the enhanced state described less directional persistence and more variation in velocity. Speed was derived as a function of these two parameters and was used here as a proxy for their joint responses, representing directional displacement over time.          To assess differences in the behavior states between experimental phases, the block-specific latent states were modeled as a function of phase-specific probabilities, <em>Z<sub> t </sub></em>~ Bernoulli (<em>p</em><sub>phase<em>t</em></sub>), to learn about the probability <em>p</em><sub>phase</sub> of being in an enhanced state during each phase. For each pre-exposure, exposure, and post-exposure phase, this probability was assigned a flat uniform (0,1) prior probability. The model was programmed in R (R version 3.6.1; The R Foundation for Statistical Computing) with the <em>nimble</em> package (de Valpine et al. 2020) to estimate posterior distributions of model parameters using Markov Chain Monte Carlo (MCMC) sampling. Inference was based on 100,000 MCMC samples following a burn-in of 100,000, with chain convergence determined by visual inspection of three MCMC chains and corroborated by convergence diagnostics (Brooks and Gelman, 1998). To compare behavior across phases, we compared the posterior distribution of the <em>p</em><sub>phase</sub> parameters for each response variable, specifically by monitoring the MCMC output to assess the “probability of response” as the proportion of iterations for which <em>p</em><sub>exposure </sub>was greater or less than <em>p</em><sub>pre-exposure</sub> and the “probability of persistence” as the proportion of iterations for which <em>p</em><sub>post-exposre </sub>was greater or less than <em>p</em><sub>pre-exposure</sub>. These probabilities of response and persistence thus estimated the extent of separation (non-overlap) between the distributions of pairs of <em>p</em><sub>phase</sub> parameters: if the two distributions of interest were identical, then p=0.5, and if the two were non-overlapping, then p=1. Similarly, we estimated the average values of the response variables in each phase by predicting phase-specific functions of the parameters: Mean.response<sub>phase </sub>= exp(l<sub>0</sub> + l<sub>1</sub><em>p<sub>phase</sub></em>) and simply derived average speed as the mean of the speed estimates for 5-second blocks in each phase.
description: # Behavioral responses of common dolphins to naval sonar Four data files
 are attached. The first two present counts of the number of common dolphin
 subgroups that were visually observed (header "subgroups") and
 counts of the whistles (header "whistles") that were
 acoustically recorded in five-second blocks during three experimental
 phases (pre-exposure, exposure, and post-expose) of a controlled exposure
 experiment for common dolphins exposed to sonar. The number of subgroups
 was recorded during periodic observations and assumed to be constant
 across time blocks between observations. However, the number of subgroups
 was treated as missing data (indicated by "NA") in the five
 blocks up to 30 seconds before each change was noted to introduce prior
 uncertainty about the precise timing of changes. Values were later
 estimated for each of these NAs from a Bayesian Hidden Markov model (HMM).
 This was the 12th experiment conducted in the year 2021, hence the
 inclusion of "2012_12" in the filenames:
 databyblock_2021_12_subgroups.csv databyblock_2021_12_whistles.csv In each
 of these, the column headers are: "phase" = 1 for pre-exposure,
 2 for exposure and 3 for post-exposure "blockseq" = sequentially
 numbered 5-second blocks throughout the experiment "startSec" =
 the first second of each block, with seconds numbered sequentially through
 the experiment Two further datafiles relate to movements of a focal
 subgroup of common dolphins which was geolocated using spatially-explicit
 photogrammetry from an Unoccupied Aerial System (UAS or drone) platform
 during the same experiment. The first
 "databyblock_2021_12_movement.csv" simply defines the 5-second
 blocks throughout the experiment with the same headers of
 "phase", "blockseq" and startSec", as defined
 above. The second ("Track_2021_12_movement.csv) has additional
 columns to identify the "latitude" and "longitude" of
 the subgroup's location during sampling intervals spaced as
 frequently as 1 second within these blocks. The time of sampling is given
 in "gmt" and numeric "time" (the number of seconds
 since the beginning of 1970), as well as the consecutive second throughout
 the experiment ("timeSec") and the consecutive 5-second block
 ("blockseq") in which this second occurred. ## Code/Software
 These data on movement, subgroups, and whistles were modelled using
 HMM's fit in R (R version 3.6.1; The R Foundation for Statistical
 Computing) using the three .R scripts: HMM script_movement.R HMM
 sript_subgroups.R HMM script_whistles.R These models were fit separately
 using the nimble package (de Valpine et al. 2020): de Valpine, P.,
 Paciorek, C., Turek, D., Michaud, N., Anderson-Bergman, C., Obermeyer, F.,
 Cortes, C.W., Rodrìguez, A., Lang, D.T, Paganin, S. 2020. Nimble: MCMC,
 Particle Filtering, and Programmable Hierarchical Modeling. R package
 version 0.9.1.
 [https://CRAN.R-project.org/package=nimble](https://CRAN.R-project.org/package=nimble)