JIP Aversion Modeling Final Report. Adam S. Frankel, Ph.D. William T. Ellison, Ph.D. Andrew W. White, Ph.D. Kathleen J. Vigness Raposa, Ph.D.

Transcription

1 JIP Aversion Modeling Final Report Adam S. Frankel, Ph.D. William T. Ellison, Ph.D. Andrew W. White, Ph.D. Kathleen J. Vigness Raposa, Ph.D. Marine Acoustics, Inc. MAI July 2016 TN

2 Table of Contents INTRODUCTION... 3 METHODS... 4 SOURCE AND PROPAGATION MODELING... 4 ANIMAT MODELING... 7 MODELED ANIMAL TYPES... 9 EVALUATION OF MODELING RESULTS RESULTS SOURCE CHARACTERISTICS ACOUSTIC SOUND FIELDS (TL SLICES) EXPOSURE RESULTS LITERATURE CITED APPENDIX I: NUMBERS OF ANIMALS EXPOSED TO SOUND LEVELS EXCEEDING CRITERIA THRESHOLDS

3 Introduction Marine Acoustics, Inc. (MAI) modeled a seismic array and its underwater acoustic propagation during exemplar one month exploration surveys in the Gulf of Mexico to examine marine mammal exposure estimates over a selected combination of source and animal movement parameters. Four selected marine mammal types, representing one low (LF), two mid (MF) and one high (HF) frequency members of the hearing groups defined by Southall et al. (2007), as well as two survey configurations, representing nominal 2D and 3D airgun array surveys, as well as a stationary source survey, were evaluated in this parametric study. The acoustic exposure and animal response were estimated using the Acoustic Integration Model (AIM). Four source/animal simulation cases were undertaken: (1) stationary source with stationary but diving animals, (2) moving source with stationary but diving animals, (3) moving source with moving and diving animals, and (4) moving source with moving and diving animals with aversive behaviors to received sound pressure levels (SPL). These movements were convolved with the output of the source acoustic propagation model to calculate the full 30 day exposure histories for each simulated animal for each survey configuration. These results were frequency weighted using no weighting, M weighting (Southall et al., 2007), Navy Type II weighting (Finneran and Jenkins, 2012) and proposed NOAA guidance (NOAA, 2016). The resultant 30 day exposure histories for each animal were evaluated using both traditional metrics (unweighted 160 db SPL for behavior, 180 db SPL for injury) as well as a variety of TTS and PTS thresholds from Southall et al. (2007), Finneran and Jenkins (2012) and NOAA (2016). This study significantly parallels the modeling assessment presented in Ellison et al. (2016). That study provides discussion and evaluation techniques that are complementary to this report, particularly with regard to the evaluation of proportionally scaled aversion of animals to received sound pressure levels. In Ellison et al. (2016), the full 2008 fall bowhead migration (ca. 10,000 animals) were individually assessed during a 47 day period covering the population s westward migration past a simulation of nine selected industry noise sources that were operating in that area and time. The nominal passage time in the Ellison et al. (2016) study was approximately one week for an individual animal. The underlying objective in both of these studies was to model each individual animal (animat) continuously for the entire period of potential exposure, with a dosimeter recording of exposure history. 3

4 Methods Source and Propagation Modeling Acoustic Source Model The current study used a combination of methods to evaluate the source characteristics of the airgun array described in Table 1. The first step was to input a full description of the airgun into the Gundalf model (Hatton, 2008) that predicted the array source spectrum used to calculate the 1/3 octave SEL source levels for the array from 10 Hz to 1 khz. The directivity pattern of the array was calculated using the volumetric beam pattern generator module in the CASS GRAB package (Burdic, 1984; Weinberg, 2004). The inputs to the module were the x, y, and z location of each gun and the relative amplitude of each gun, represented as the cube root of its volume. The directivity pattern was generated for every two degrees of declination (vertical direction) from + 90 to 90, every 10 degrees in azimuth (horizontal direction) and for each 1/3 octave band center frequency from 10 Hz to 1 khz. Acoustic Propagation Modeling The sound field created by the proposed airgun array was modeled using the rangedependent acoustic model (RAM). RAM is a PE based model that incorporates a geoacoustic ocean bottom model that accounts for bottom loss due to shear wave propagation (Collins, 1993). Physical Environmental Inputs The OAML Generalized Digital Environmental Model (GDEM) Version 3.0 database (Naval Oceanographic Office, 2003) was accessed for sound velocity profiles. The sound velocity profiles for July through December are shown below (Figure 1). The October profiles were used for propagation modeling. Geoacoustic model parameters were extracted from the Gulf of Mexico G&G Activities Draft Programmatic EIS, Appendix D, Table 53 (Zeddies et al., 2015). These are shown in Table 2. 4

5 Table 1: Detailed specifications of the 5,110 in 3 airgun array String Element Pressure Volume (psi) (cu. In.) X (m) Y (m) Z (m)

6 Figure 1: Sound velocity profiles for July through December for the modeling location in the Gulf of Mexico. More detailed structure of the upper portion of the water column is presented in the right panel. Depth below seafloor (m) Table 2: Geoacoustic parameters used for acoustic propagation modeling Material Density P-wave speed P-wave attenuation S-wave speed S-wave attenuation (g/cm 3 ) (m/s) (db/λ) (m/s) (db/λ) 0 20 Silt φ= > N x 2 D Volume Approximation The RAM acoustic propagation model is a two dimensional model; it returns a sound field for a single bearing with values as a function of range and water depth. This representation is sometimes referred to as a slice or azimuth. Because acoustic propagation is dependent on range varying bathymetry and sound velocity profiles, 36 azimuths at 10 intervals to a maximum range of 50 kilometers from the modeling location (50 meter increments) were calculated for this study. Frequency Dependence: Summing Over 1/3 Octave Bands Airgun sources are impulsive and produce broadband signals. Because RAM is a single frequency acoustic propagation model, the transmission loss was calculated for each 6

7 1/3 octave band center frequency from 10 Hz to 1 khz. Each transmission loss slice was subtracted from its corresponding source level value to produce a slice of received sound exposure levels. These separate 1/3 octave band sound level fields were then combined as intensities to produce a broadband, three dimensional acoustic field for the modeling location. This process was repeated with source level values that had been adjusted with auditory weighting functions, so that the final output included multiple SEL acoustic fields: a flat or unweighted acoustic field, as well as nine weighted acoustic fields that incorporated the M weighting, Navy Type II and NOAA (2016) auditory weighting functions for low, mid, and high frequency cetaceans. The source level calculated from the array signature is a Sound Exposure Level (SEL) measure. However, RMS values are also needed for behavioral response evaluation. The formulas described in the Final Programmatic Environmental Impact Statement (EIS) for the Atlantic Outer Continental Shelf Proposed Geological and Geophysical Activities: Mid Atlantic and South Atlantic Planning Areas ( G G PEIS/#Final%20PEIS) were applied to the SEL versions of the acoustic fields to create their RMS pressure equivalents. Animat Modeling The Acoustic Integration Model (AIM) is an individual based, Monte Carlo statistical model designed to predict the exposure of receivers to any stimulus propagating through space and time, which in this analysis is acoustic energy (Frankel et al., 2002). The central component of AIM is the animat movement engine, where parameters control the speed and direction of movement of animats in three dimensional space at specified time intervals to create a full four dimensional simulation of the proposed survey. AIM has been used for many environmental compliance documents and was approved by external Center for Independent Experts (CIE) review (Cordue, 2006). Three different source movement patterns were used: 1) Stationary, 2) 2 D survey (Figure 2), and 3) 3 D survey (Figure 3). 7

8 Figure 2: The track lines of the 2-D survey (large spatial area) are shown. Nominal spacing between tracks was 10 km. 8

9 Figure 3: The tracklines of the 3-D survey (small spatial area) are shown. Nominal track spacing was 500 m. A separate simulation was created and run for each combination of marine mammal types (n=4), animal movement pattern (n=3), and source geometry (n=2). An additional four simulations were run with the stationary source and animal types modeled as stationary but diving, resulting in a total of 28 exposure scenarios. Each simulation was run for 30 days using a time step of 30 seconds. The nominal firing time of the airgun array was 10 seconds. Therefore it was assumed that the received level at each 30 second interval represented three airgun shots. Modeled Animal Types Four animal types were chosen to provide representative variations in behavioral dive and movement patterns as well as the respective hearing groups of marine mammals in the Gulf of Mexico. LF (low frequency hearing) whales were based on Bryde s whales as the only mysticete normally found in the Gulf of Mexico. Shallow and deep diving MF (midfrequency hearing) animals were based on bottlenose dolphins and sperm whales respectively. Finally HF (high frequency) animals were based Kogia species, the only highfrequency (HF) hearing animal found in the Gulf of Mexico. 9

10 Animal movement cases: Three animal movement patterns were investigated. The simplest was horizontally stationary but diving animals. This was examined with a stationary source and the 2D and 3D surveys. Next, fully moving four dimensional (space and time) animats were assessed for exposure from the array performing 2D and 3D survey patterns. Finally, fully moving animals with aversion were modeled with the array performing 2D and 3D survey source patterns. Animats were programmed in AIM with behavioral values describing dive depth, surfacing and dive durations, swimming speed, and course change. A minimum and maximum value for each of these parameters is specified. The result is a stochastically generated path over time. The behavioral parameters used to program these animats were taken from the literature and are shown in Table 3. Figure 4 provides an example of the full 30 day track for an individual animat representing each of the four animal types. Table 3: Animat Behavioral Parameters Animal type LF Shallow MF Deep MF HF Top Depth (m) Bottom Depth (m) Time min/ max (min.) Heading Variance (deg) Speed min / max (km/h) Heading Variance Turn Time (sec) Percentage of Time in Behavioral State Min Water Depth (m) Behavioral State Surface 0-5 1/1 30 2/ Dive / / Dive / / Dive / / Dive / / Surface 0-6 1/1 30 2/ Dive / / Dive /2 90 2/ Dive /3 30 2/ Dive /4 90 2/ Dive /6 90 2/ Surface /9 10 1/ Dive / / Dive / / Surface 0-5 1/2 30 1/ Dive / / Dive / /

11 Figure 4: One example of the movement track for each animal type of animat is shown. The white lines represent the 30-day tracks for each animal type. The residency pattern for any animal type is one of the key features that would contribute significantly to the accuracy of long term modeled assessments in a given region. One can visualize the need for residency assessment (e.g., from long term tag data) by examining the tracks shown in Figure 4, where the differences in the spatial extent of the month long tracks of the different animal type can be easily seen. The HF and shallow MF cetaceans have a higher proportion of long, mostly linear travel, whereas the LF and deep MF cetaceans have more circuitous and twisting movement patterns. A candidate metric to describe such an overall movement pattern is the linearity index (Baker and Herman, 1989). This is simply the ratio of the net movement (i.e., a straight line distance between the first and last point of the track) divided by the total distance swum. A low linearity score means the path of the animal was fairly circuitous, which would represent a fairly resident animal. A high linearity score means the path of the animal is fairly straight, which would represent an animal traveling across a greater spatial extent. The mean and standard deviations of the linearity scores for the four animal types are shown in Table 4. 11

12 Table 4: The linearity indices (mean and standard deviation) of the four modeled animal types. The modal speed and shallow water limits are also listed for each, as defined in AIM. There was no deep-water limit for any animal type. Linearity Modal Speed Shallow water Animal Type Mean S.D. (km/h) limit (m) Shallow LF Cetacean HF Cetacean Deep MF 4.5 Cetacean LF Cetacean Animat Spatial Distribution In a typical environmental assessment, the modeled animal seasonal distributions would be informed by the most current animal distribution databases, e.g. Roberts et al. (2016). However, for this parametric evaluation, we elected to uniformly populate each animal type throughout the entire Gulf region, constrained only by limiting water depth and boundary limitations. This allowed us to fully evaluate the key parameters of hearing type and shallow vs. deep divers across the various combinations of source configurations and hearing weighting functions. Thus, animats were distributed over the entire northern Gulf of Mexico using a model density of 0.01 animats per square kilometer. This produced simulations with 8,000 12,000 animats per simulation. Distribution to the north and west was constrained by land. The southern boundary was the 22 N latitude line. The eastern boundary was Florida and the 80.5 W longitude line between Florida and Cuba. The shallow water depth limitations (i.e., aversion to water depth set in AIM to prevent animals from moving into shallow waters) are provided in Table 4 and illustrated in Figure 5 for shallow MF and HF cetaceans. 12

13 Figure 5: Exemplar distributions of shallow MF cetaceans (red) and HF cetaceans (yellow). The displayed animal distributions have lower densities than the actual simulations to allow the bathymetry to be seen. With regard to LF cetaceans, Bryde s whales are known to be concentrated near the De Soto Canyon off northwest Florida. However, if the LF cetaceans were constrained to that area, then no meaningful exposure results would have been obtained. Therefore, LF cetaceans were distributed throughout the Gulf of Mexico. Again, this was done only for the purpose of this study and does not represent the actual distribution of LF cetaceans in the Gulf of Mexico. Thus, the LF results provided here can be viewed to some degree as a proxy for exposure assessment in regions where mysticetes are more widely distributed. Auditory Weighting Functions A key component of the study is to compare the effects of different frequency weightings on the level of exposure predicted for each source and animal movement combination. Four frequency weightings were employed. These were 1) unweighted, 2) M weighting (Southall et al., 2007), 3) Navy Type II (Finneran and Jenkins, 2012), and 4) NOAA (2016) proposed acoustic guidance, as depicted in Figure 6. 13

14 Figure 6: The weighting functions for low, mid and high frequency cetaceans. Three weighting functions are presented, the M-weighting (Southall et al. 2007), Navy Type II (Finneran and Jenkins, 2012) and NOAA draft acoustic guidance (NOAA 2016). Aversion Parameters A nominal set of aversion parameters for received sound pressure level was developed in Ellison et al. (2016) and a complementary version was used for this study. These can be summarized as an increasing probability of aversion as the received sound level increases. Specifically, the probability of aversion goes from 5% to 50% to 95% at received SPLs of 140, 160 and 180 db SPL, respectively. The specific aversion parameters used in AIM are shown in Table 5 along with the angular degree of aversion ( turn away ) for the different levels. Table 5: Aversion Parameters Received SPL (unweighted) Probability of Aversion Aversion Angle 140 db 5% 20 away from source 150 db 25% 30 away from source 160 db 50% 40 away from source 170 db 75% 50 away from source 180 db 95% 60 away from source Evaluation of Modeling Results Each simulation produced approximately 10 GB of model output and was analyzed in a two tier fashion. The first step was conducted on one hour of model output at a time. Received levels during the turns between survey legs were set to zero. Then the hourly maximum received SPL and the total sound intensity, cumulative SEL (CSEL), for each 14

15 animat was calculated and stored in a summary file. This reduced the number of observations from 86,400 to 720 rows. These hourly metrics were then used to create the maximum unweighted received sound pressure level (MSPL) for each 24 hr period of the 30 day survey duration modeled for each animat. Likewise, cumulative SEL metrics were also calculated for each 24 hr period of the 30 day survey duration modeled for each animat. This calculation included the SEL correction (added 5 db) needed to account for the three airgun array shots that occurred during each 30 second model step. Mean MSPL and CSEL values were then calculated for each animat. These were compared to the 160/180 db SPL criteria for traditional Level B and Level A exposures as well as the SEL criteria for TTS and PTS in Southall et al. (2007), Finneran and Jenkins (2012) and NOAA (2016). Southall et al. (2007) only proposed a criterion for PTS. A TTS criterion for M weighting was created by subtracting 20 db from the PTS criterion. The Southall et al. (2007) criteria were also used for the unweighted CSEL metrics, as no previous criteria existed. The mean number of daily exposures that exceeded those criteria (i.e., takes) resulted from this approach. As stated, this study is focused on the effects of survey design and animal behavior patterns on exposure estimates and not assessing actual environmental impact. Therefore no correction was made to scale the modeled animal densities to local animal densities, as this would only add confusion and an additional source of uncertainty. Results Source Characteristics The Gundalf model was used to predict the source level and spectrum of the airgun array. The resulting source spectra are shown in Figure 7. Both spectral and 1/3 octave band values are presented. These are the unghosted versions of the spectrum, as the propagation model explicitly considers the effect of surface reflection. 15

16 Figure 7: Source spectra for the airgun array Acoustic Sound Fields (TL Slices) The unweighted sound field results were used to evaluate the effect of source movement pattern (i.e., 2D v. 3D) as well as animat behavior (aversion v. none, full 4 D movement v. surface and diving only). The three weighting functions (M, Type II, and NOAA) were applied directly to the sound fields themselves, and are illustrated below as to their application across the three animal groups (LF, MF and HF; Figures 8, 9, and 10, respectively). 16

17 Low Frequency Weighted Sound Fields Figure 8: Airgun array sound field with four different low-frequency weighting functions applied. Mid Frequency Weighted Sound Fields Figure 9: Airgun array sound field with four different mid-frequency weighting functions applied. 17

18 High Frequency Weighed Sound Fields Figure 10: Airgun array sound field with four different high-frequency weighting functions applied Exposure Results For each animat, 30 daily values of unweighted MSPL and CSEL for each simulation scenario was examined and analyzed on sequential 24 hr periods, as well as continuous measurements over the entire 30 day duration. Figure 11 displays the 30 day track of a selected Deep diving MF cetacean with an overlay of the 2D survey grid. Figure 12 provides the recorded exposure history (hourly MSPL) for this same animal over the 30 day period. In this example, it can be inferred that during the middle of the 30 day period the source tracks and animal track did not significantly overlap since the animal had no MSPL greater than 160 db. 18

19 Figure 11: Illustration of 2-D Survey Grid and a single animat track. Figure 12: Exposure History of the animat in the previous figure. Received Sound Pressure Levels are shown as blue circles. The alternating vertical gray and white bars indicate successive days 19

20 Comparison of 2D v. 3D Acoustic Exposures Separate histograms were calculated of the number of animals exposed at unweighted MSPLs and CSELs over the entire 30 day period for each animal type. These figures compare the unweighted exposure levels between a 2 D and a 3 D source pattern for the four different animal type (Figures 13 16). There are no meaningful differences among the animal types in the shapes of these distributions. However, the numbers of exposures, both MSPL and CSEL, are greater for the 2D survey, as the survey covered a significantly larger area (greater potential for more animals to be exposed) than the 3D survey. The numbers of exposures are also higher for the Shallow diving MF cetaceans as they are principally in the upper water column where the exposure levels are more consistently higher with range than at depth. Figure 13: Distribution of MSPL and CSEL values for LF cetaceans with full 4-D movement with no aversion behavior 20

21 Figure 14: Distribution of MSPL and CSEL values for shallow MF cetaceans with full 4-D movement with no aversion behavior Figure 15: Distribution of MSPL and CSEL values for deep diving MF cetaceans with full 4-D movement with no aversion behavior 21

22 Figure 16: Distribution of MSPL and CSEL values for HF cetaceans with full 4-D movement with no aversion behavior. Comparison of 2D v. 3D Acoustic Exposures vs. Animat Movement Behavior Figure 17 presents the number of mean daily MSPL exposures > 160 db for all animal types for both survey types and all three movement modalities (full movement with and without aversion, and diving only). The overall results are consistent with the distributions shown in Figures 13 16, with the 2D survey having more exposures > 160 db than the 3D survey, as previously discussed. The explanation for the apparently larger number of exposures for full movement animats over diving only animats would appear to be because the diving only animats will only be exposed at higher levels at the moment the survey vessel passes close by, whereas the moving animals (without aversion) may in their general circuitous meanderings cross over the source path more than once. The most striking result is the number of exposures of animals allowed to avert, which results in the lowest exposure count of all scenarios. Figures 18 through 25 display the number of animals exposed at unweighted MSPLs and CSELs over the entire 30 day period for each animal type for both survey types with aversion and without aversion. The results show the powerful effect of aversion on exposure histories. 22

23 Figure 17: The distributions of daily 160 db exposures for all animal types are shown for the different source movement patterns (2-D, 3-D) and animat behavior (No Aversion, Surface and Dive only, and With Aversion). 23

24 Figure 18: Shallow MF cetacean, 2-D survey, with and without aversion Figure 19: Shallow MF cetacean, 3-D survey, with and without aversion 24

25 Figure 20: LF cetacean, 2-D survey, with and without aversion Figure 21: LF cetacean, 3-D survey, with and without aversion 25

26 Figure 22: HF cetacean, 2-D Survey, with and without aversion Figure 23: HF cetacean, 3-D survey, with and without aversion 26

27 Figure 24: Deep-diving MF cetacean, 2-D survey, with and without aversion Figure 25: Deep-diving MF cetacean, 3-D survey, with and without aversion In Appendix I we summarize each of the exposure assessment metrics beginning with a summary for each animal type using the historic behavior threshold SPL > 160dB re 1 Pa, and the historic injury threshold >180dB re 1 Pa. The tables tabulating PTS exposure clearly show the dramatic effect of aversion, as do Figures 18 25, but also the varying effects of the three auditory weighting functions. Consideration of Combination of Auditory Weighting Function and Criteria The auditory weighting/criteria assessments are more complex in explanation due to subtle effects of the weighting function shapes for M, Type II and NOAA (2016) in relation to the changes in PTS criteria that are associated with each function. The result of 27

28 changing both weighting function and criteria seemingly provide what appears to be conflicting results for the HF animals in particular as tabulated in Table 5 of Appendix I, copied below for ease of referral. Appendix I - Table 5 Species PTS (values shown in db re 1Pa 2 -sec) 2-D Pattern Weighting Functions and Associated Criteria Stationary and AVERTING animals Unweighted 198 db M-weighted 198 db Type II 172 db Draft NOAA 155 db D Pattern Weighting Functions and Associated Criteria Stationary and AVERTING animals Unweighted 198 db M-weighted 198 db Type II 172 db Draft NOAA 155 db In this table, the Type II function results in higher exposure estimates in the final assessment than the much steeper NOAA 2016 function. The rationale comes from a combination of two factors. First, the sound source energy is concentrated in the spectrum below 1 khz, and the low frequency bulge of the Type II weighting function allows a significant amount of that energy to be applied to the broadband received level of the HF animal. The difference in LF energy between the Type II and the NOAA functions is on the order of 30 db (the Type II function is 30 db greater than the NOAA function at LF). Secondly, the Type II HF criterion (172 db) is 17 db greater than the NOAA HF criterion (155 db), but this 17 db difference in the criteria is less than the 30 db difference in the functions. The result is higher exposures under the Type II weighting function/criteria combination. 28

29 Comparison of Long term Modeling with Current 24 hour reset based regulatory framework NOAA s acoustic guidance (NOAA, 2016) incorporates the concept of 24 hour reset in animal exposure estimates. The 24 hr reset means that the integration time for CSEL and the evaluation period for behavioral (maximum SPL) metrics are set to 24 hours. These model output values are then compared with regulatory thresholds and the number of takes is determined for a 24 hour period. Typically, these daily numbers are simply multiplied by the number of days over which the activity is anticipated to occur to produce total take estimates. This requirement has meaningful practical implications on modeling conducted to predict the impact of a human activity. Firstly, this approach does not differentiate between the number of acoustic exposures and the number of exposed individuals. An acoustic exposure is defined here as when an individual animal is exposed to a sound level that exceeds a regulatory threshold (e.g., 160 db rms for airguns). An exposed individual is an animal that experiences one or more acoustic exposures during the duration of an activity. Given animal and source movement, individual animals could experience multiple acoustic exposures within one 24 hr period or over many days of an activity. This issue is compounded when an estimate of the activity s impact on a population is computed. An activity s predicted take numbers are often compared to population size estimates. With a sufficiently long activity, there can be more 24 hour takes than there are individuals in the population. A potential solution to this quandary is illustrated through long term animat modeling. By modeling the entire duration of an activity, the number of exposures that each individual experiences, as well as the number of exposed animals and the traditional metric of the total number of exposures can be calculated. Thus in this approach, the number of exposed animals cannot exceed the number of animals in the population, and the potential impact on individual animals is elucidated through direct modeling results. In this case study, the 30 day modeling results were evaluated using an increasing window of potential exposure time. Take was defined as the number of individual animals that received a maximum RL exceeding the regulatory threshold, which was determined for an increasing period of time from one day to the full 30 days duration. The solid lines in Figures 26 and 27 show the number of exposed individuals over the entire duration of the activity. This measure of take was compared against the traditional method, where the number of takes over a 24 hour period is multiplied by the number of days of the activity, which is shown with the straight dashed lines in Figure 26 and Figure 27. In all cases, the number of individuals exposed at maximum RL as determined with the 24 hr reset time was greater than the actual number of individuals exposed at maximum RL over the duration of the activity. 29

30 Figure 26: 2-D Source. The solid lines show the increase in the number of exposed individuals at maximum RL based on direct measurement of long-term model output. The dashed lines show the number of exposed individuals at maximum RL using the 24-hour reset method. Figure 27: 3-D Source. The same presentation is repeated for a 3D source. 30

31 Another way to consider this effect is a hypothetical population of 100 individuals for which 10 individuals experience maximum RLs that exceed the regulatory threshold over a 24 hr period (i.e., the 24 hour take prediction is 10). Over a 30 day activity, the total predicted take would be 300 (30 days x 10 animals/day), or 3x the population size. One could assume that the animals in this population had extremely high residency or site fidelity. In such a case, it could be that for 30 days, the same 10 animals are exposed repeatedly. Thus the number of exposed animals was actually only 10, while 300 exposures were predicted. To examine the effect that residency might have on exposure estimates, we also calculated the number of days that each individual was exposed to an unweighted sound level of 160 db rms. The percentage of individuals exposed on multiple days was determined and compared with movement parameters. This was done for each animat species and source movement pattern (Table 6). The percentages of multiple exposures were plotted against the linearity index for each species survey type (Figure 28). Table 6: Percentage of Individuals exposed more than once. These results are for normal four-dimensional movement with no aversion using unweighted sound levels. Species Survey Type Linearity Index Dive Depth (m) Percentage of Individuals Exposed more than once Shallow MF Cetacean 2D % HF Cetacean 2D % Deep MF Cetacean 2D % LF Cetaceans 2D % Shallow MF Cetacean 3D % HF Cetacean. 3D % Deep MF Cetacean 3D % LF Cetacean 3D % 31

32 Figure 28: The percentage of indivdiuals exposed more than once is shown as a function of the linearity index of each animat. Multiple exposures increase with decreased linearity. The 3D survey produced higher takes than the 2D survey. Figure 28 shows a strong relationship between the linearity of an animat s movement and how often an individual was taken on multiple days. Considering the percentage of animals exposed more than once, a 50%value would indicate that, on average, each animat was exposed twice. Generally, as the linearity of the animat s course decreases, the amount of time spent in the same area increases. Likewise the percentage of multiple exposures increases. A more detailed analysis examined the effect of survey type, linearity index and mean animal diving depth on the percentage of multiple exposures. An ANOVA model was statistically significant (F (3,4) = 41.3, p = ). The model found that the 3D survey type, which concentrates airgun activity within a small area, had a significantly larger percentage of multiple exposures compared to the 2D survey, which operates over a larger area. Lower linearity index values contributed to a significantly larger percentage of multiple exposures than higher linearity index values. Also, deeper diving animals were found to have significantly more multiple exposures than the shallower divers. This sensitivity study shows that modeling the full duration of an activity results in a better understanding of the number of individuals exposed and the number of exposures 32

33 per individual. These are key parameters for determining potential impacts, particularly for protected species. Therefore both metrics could be evaluated and such an approach would provide insight into traditional predictions of take numbers larger than population sizes. The validity of the long duration modeling results are strongly influenced by the accuracy of the behavioral parameters used to create the simulations. Reliable data on the residency patterns and diving behaviors of species and populations being modeled is critical to valid model outputs. Considering the effect of group size on number of predicted exposures The basic modeling approach consists of creating an overpopulated simulation, where the model density is typically much greater than that of the real world situation and each animat represents an individual animal. Once the simulation has been run, the outputs are examined to determine the number of exposures that exceed regulatory thresholds. When animats are considered as individuals, the scaling relationship is quite simple, as expressed in the following equation. Typically, the number of modeled exposures is a point estimate based on the modeling results. However, an estimate of uncertainty in the exposure estimates can be calculated using resampling, in which case the modeled exposures value could be a mean of resampled estimates with an associated measure of variation (e.g., S.D. or C.V.). To move the modeling scenario towards a more realistic reflection of animal distribution, however, it is important to consider that most animals naturally occur in groups of variable size. The mean value of an exposure estimate will not change because the density of animal groups is the individual animal density value (animals/area) divided by the mean group size. The factor that will change is the measure of variation around that mean, which reflects the variability in the distribution of group size in wild populations. The most straightforward approach to incorporating group size into the effects analysis is to use a resampling procedure. In this study, a set of distributions of group sizes was created based on observed group size estimates (Maze Foley and Mullin, 2006), as shown in Table 7. Group size minimum and maximum values were created for each species using a lognormal function, rounded to integers and truncated to the largest reported group size. Both individuals and groups have a common source of variability in how many animats in a selected sample will have a received level that exceeds a regulatory threshold. Group Size introduces an additional source of variation (i.e., the number of animals represented by a single animat) not found in the individual analysis. Therefore, with two sources of variation, the a prioiri expectation is that the variance around the group based mean estimate should be greater than that for individuals. 33

34 Table 7: Group Size Data for Gulf of Mexico Animat Type Mean Group Size SE Min Max N LF Cetacean Deep MF Cetacean HF Cetacean Shallow MF Cetacean The resampling procedure selected 100 samples from the acoustic exposure data and the group size distribution. The number of samples that exceeded a regulatory threshold (e.g., 160 db RMS) along with their corresponding group size was determined. The sum of the group size samples was returned as the number of individuals exposed to that threshold level. This process was repeated 10,000 times to produce a resampled distribution of the number of exposed individuals. The mean and standard deviation of these resampled distributions were calculated. Thus the resampling approach provides an additional method to estimating confidence limits around the exposure estimate. The resampling procedure was conducted for the 2 D and 3 D survey geometries with fully moving (non averting) animats. The procedure was repeated for animats considered as individuals as well as groups. The results are presented in Figure 29 and Figure 30. The central dot for each species represents the mean of the distribution and the error bars are +/ one standard deviation. The blue lines represent resampling distributions based on individuals (group size = 1) and the red lines represent distributions based on actual group sizes found in the Gulf of Mexico. 34

35 Figure 29: Resampling Results for 2D-Survey showing modeled 160 db exposures. Figure 30: Resampling Results for the 3-D Survey showing modeled 160 db exposures. There is no meaningful difference in the mean exposure estimates. However, the variation around these estimates are greater when group sizes are considered. As expected, incorporating group size had no meaningful effect on the mean estimate of the number of exposures. For example, shallow diving MF cetaceans in the 3 D survey scenario had a point estimate of 829 Level B exposures. The resampled values using animats as individual was 826.5, and considering animats as groups returned a mean of

36 In all cases the standard deviations were greater when animats were considered as groups rather than individuals. Furthermore, the size of the standard deviation increased as a function of group size. Thus it can be concluded that incorporating group size estimate data will (realistically) increase the uncertainty around the mean number of predicted exposures without meaningfully changing its value. Considering the Effect of Movement of Sources and Some of the simulations included stationary sources (SS) and/or marine mammal animats that could surface and dive only (SD) but not move horizontally. The number of individuals exposed over the 30 day period to 160 db was examined as a function of species as well as source and receiver behavior. The results are shown in Figure 31. The first two sets of columns represent the results with 2D and 3D surveys, respectively, with normal animat behavior and no aversion response (NoAv). As reported previously, the 2D surveys have a higher number of exposures per species than do the 3D surveys. The next two sets of bars represent 2D and 3D surveys, respectively, with animats that surface and dive only (SD). The reduction in the number of exposures is clear. Again, the higher number of exposures under the 2D case reflects the larger area covered by the survey vessel. Finally, the lowest numbers of behavioral exposures are seen with a stationary source and animats that cannot move horizontally. Figure 31: Comparison of Level B exposures between moving and stationary sources and receivers 36

37 Figure 32: Comparison of TTS exposures between moving and stationary sources and receivers. When the SEL metrics are considered for TTS (Figure 32) and PTS (Figure 33) the pattern changes somewhat. In Figure 32 the relative increase in HF cetacean values compared to the other species is due to the low TTS threshold for high frequency hearing animals. Overall, the first three sets of bars largely show the same pattern as the Level B exposure results. However the 3D and stationary sources with surfacing and diving only animats have relatively elevated values compared to the Level B exposures. Again this is due to the limited scope of source movement and the inability of receiver animats to move away. This effect is clearly visible in the PTS results (Figure 33). There are barely any modeled PTS exposures in the realistic scenarios. However, in the 3D and stationary source scenarios, with receiver animats that cannot move away, the number of PTS exposures is much greater than elsewhere. This is the clear result of animats that remain in position with a source that moves little or not at all, and the animats continue to accrue acoustic energy, leading to these artificially high PTS exposure numbers. 37

38 Figure 33: Comparison of PTS exposures between moving and stationary sources and receivers 38

39 Literature Cited Baker, C. S., and L. M. Herman. (1989). The behavioral responses of summering humpback whales to vessel traffic: Experimental and opportunistic observations. Honolulu Burdic, W. S. (1984). Underwater Acoustic System Analysis. Englewood Cliffs, NJ: Prentice Hall, Inc. Collins, M. D. (1993). A split step Padé solution for the parabolic equation method. The Journal of the Acoustical Society of America, 93(4), Cordue, P. (2006). Summary Report: Review of the Acoustic Integration Model September 2006, Washington DC Ellison, W. T., R. Racca, C. W. Clark, B. Streever, A. S. Frankel, E. Fleishman, R. Angliss, J. Berger, D. Ketten, M. Guerra, M. Leu, M. McKenna, T. Sformo, B. Southall, R. Suydam, and L. Thomas. (2016). Modeling the aggregated exposure and responses of bowhead whales Balaena mysticetus to multiple sources of anthropogenic underwater sound. Endangered Species Research, 30, doi: /esr00727 Finneran, J. J., and A. K. Jenkins. (2012). Criteria and Thresholds for U.S. Navy Acoustic and Explosive Effects Analysis Frankel, A. S., W. T. Ellison, and J. Buchanan. (2002). Application of the Acoustic Integration Model (AIM) to predict and minimize environmental impacts. IEEE Oceans 2002, Hatton, L. (2008). Gundalf an airgun array modelling package. Retrieved from Maze Foley, K., and K. D. Mullin. (2006). Cetaceans of the oceanic northern Gulf of Mexico: Distributions, group sizes and interspecific associations. Journal of Cetacean Research and Management, 8(2), National Oceanic and Atmospheric Administration (NOAA). (2016). Proposed Changes To: National Oceanic And Atmospheric Administration Draft Guidance For Assessing The Effects Of Anthropogenic Sound On Marine Mammal Hearing Underwater Acoustic Threshold Levels For Onset Of Permanent And Temporary Threshold Shifts Silver Spring Naval Oceanographic Office. (2003). Database description for the generalized digital environmental model (GDEMV) (U), version 3.0 Southall, B. L., A. E. Bowles, W. T. Ellison, J. J. Finneran, R. L. Gentry, C. R. Greene, Jr., D. Kastak, D. R. Ketten, J. H. Miller, P. E. Nachtigall, W. J. Richardson, J. A. Thomas, and P. 39

40 L. Tyack. (2007). Marine mammal noise exposure criteria: Initial scientific recommendations Aquatic Mammals, 33(4), Weinberg, H. (2004). CASS User's Guide. Anton International Corporation Zeddies, D. G., M. Zykov, H. Yurk, T. Deveau, L. Bailey, I. Gaboury, R. Racca, D. Hannay, and S. Carr. (2015). Acoustic Propagation and Marine Mammal Exposure Modeling of Geological and Geophysical Sources in the Gulf of Mexico: Annual Acoustic Exposure Estimates for Marine Mammals. JASCO Document 00976, Version 2.0. Technical report by JASCO Applied Sciences for Bureau of Ocean Energy Management (BOEM) 40

41 Appendix I: Numbers of exposed to Sound Levels exceeding criteria thresholds Note that the numerical exposure and take values resulting from this sensitivity study were based on a model density of 0.01 animats per square kilometer for each animal type, and do not represent actual animal exposures. Furthermore, the LF cetacean type was distributed throughout the Gulf of Mexico for comparison purposes only; this distribution does not represent known LF cetacean distributions in the region. Stationary Table I db 2-D Pattern and AVERTING animals Animal Type Shallow-diving MF cetacean LF Cetacean HF Cetacean Deep-diving MF cetacean Stationary 3-D Pattern and AVERTING animals Shallow-diving MF cetacean LF Cetacean HF Cetacean Deep-diving MF cetacean

42 Stationary Table I db 2-D Pattern and AVERTING animals Animal Type Shallow-diving MF cetacean LF Cetacean HF Cetacean Deep-diving MF cetacean Stationary 3-D Pattern and AVERTING animals Shallow-diving MF cetacean LF Cetacean HF Cetacean Deep-diving MF cetacean

43 Table I-3 Shallow-diving MF cetacean PTS (values shown in db re 1 Pa 2 -sec) 2-D Pattern Weighting Functions and Associated Criteria Stationary and AVERTING animals Unweighted- 198 db M-weighted 198 db Type II db Draft NOAA 185 db Weighting Functions and Associated Criteria Stationary 3-D Pattern and AVERTING animals Unweighted db M-weighted 198 db Type II 198 db Draft NOAA 185 db

44 Table I-4 LF Cetacean PTS (values shown in db re 1 Pa 2 -sec) 2-D Pattern Weighting Functions and Associated Criteria Stationary and AVERTING animals Unweighted 198 db M-weighted 198 db Type II db Draft NOAA 183 db Weighting Functions and Associated Criteria Stationary 3-D Pattern and AVERTING animals Unweighted 198 db M-weighted 198 db Type II 198 db Draft NOAA 183 db

45 Table I-5 HF Cetacean Species PTS (values shown in db re 1 Pa 2 -sec) 2-D Pattern Weighting Functions and Associated Criteria Stationary and AVERTING animals Unweighted 198 db M-weighted 198 db Type II 172 db Draft NOAA 155 db Weighting Functions and Associated Criteria Stationary 3-D Pattern and AVERTING animals Unweighted 198 db M-weighted 198 db Type II 172 db Draft NOAA 155 db

46 Table I-6 Deep-diving MF cetacean PTS (values shown in db re 1 Pa 2 -sec) 2-D Pattern Weighting Functions and Associated Criteria Stationary and AVERTING animals Unweighted 198 db M-weighted 198 db Type II 198 db Draft NOAA db Weighting Functions and Associated Criteria Stationary 3-D Pattern and AVERTING animals Unweighted 198 db M-weighted 198 db Type II 198 db Draft NOAA 185 db