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A simulation approach to assessing environmental risk of sound exposure to marine mammals

View Article: PubMed Central - PubMed

ABSTRACT

Intense underwater sounds caused by military sonar, seismic surveys, and pile driving can harm acoustically sensitive marine mammals. Many jurisdictions require such activities to undergo marine mammal impact assessments to guide mitigation. However, the ability to assess impacts in a rigorous, quantitative way is hindered by large knowledge gaps concerning hearing ability, sensitivity, and behavioral responses to noise exposure. We describe a simulation‐based framework, called SAFESIMM (Statistical Algorithms For Estimating the Sonar Influence on Marine Megafauna), that can be used to calculate the numbers of agents (animals) likely to be affected by intense underwater sounds. We illustrate the simulation framework using two species that are likely to be affected by marine renewable energy developments in UK waters: gray seal (Halichoerus grypus) and harbor porpoise (Phocoena phocoena). We investigate three sources of uncertainty: How sound energy is perceived by agents with differing hearing abilities; how agents move in response to noise (i.e., the strength and directionality of their evasive movements); and the way in which these responses may interact with longer term constraints on agent movement. The estimate of received sound exposure level (SEL) is influenced most strongly by the weighting function used to account for the specie's presumed hearing ability. Strongly directional movement away from the sound source can cause modest reductions (~5 dB) in SEL over the short term (periods of less than 10 days). Beyond 10 days, the way in which agents respond to noise exposure has little or no effect on SEL, unless their movements are constrained by natural boundaries. Most experimental studies of noise impacts have been short‐term. However, data are needed on long‐term effects because uncertainty about predicted SELs accumulates over time. Synthesis and applications. Simulation frameworks offer a powerful way to explore, understand, and estimate effects of cumulative sound exposure on marine mammals and to quantify associated levels of uncertainty. However, they can often require subjective decisions that have important consequences for management recommendations, and the basis for these decisions must be clearly described.

No MeSH data available.


Comparing the effect of M‐ versus A‐weightings on predicted mean SELs for two species over time—M‐weightings giving the upper curves. The horizontal lines indicate (a) Dashed lines ‐ the Southall et al. (2007) threshold for PTS in gray seals (203 dB) and harbor porpoise (215 dB) when exposed to nonpulsed sound and (b) Solid lines ‐ thresholds for PTS for use with A‐weighting. The latter are 95 dB above the threshold of hearing (Heathershaw et al., 2001), which equates to 166 dB for gray seals and 175 dB for harbor porpoise at 1 kHz. Gray shading gives a 95% prediction interval, that is, the central 95% of SELs calculated for simulated animals. Note nonlinear x‐axis for display, and sound levels are dB re 1 μPa2/s
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ece32699-fig-0003: Comparing the effect of M‐ versus A‐weightings on predicted mean SELs for two species over time—M‐weightings giving the upper curves. The horizontal lines indicate (a) Dashed lines ‐ the Southall et al. (2007) threshold for PTS in gray seals (203 dB) and harbor porpoise (215 dB) when exposed to nonpulsed sound and (b) Solid lines ‐ thresholds for PTS for use with A‐weighting. The latter are 95 dB above the threshold of hearing (Heathershaw et al., 2001), which equates to 166 dB for gray seals and 175 dB for harbor porpoise at 1 kHz. Gray shading gives a 95% prediction interval, that is, the central 95% of SELs calculated for simulated animals. Note nonlinear x‐axis for display, and sound levels are dB re 1 μPa2/s

Mentions: The choice of weighting scheme, even in combination with its associated threshold, had a marked effect on the proportion of the simulated population estimated to experience PTS (Figure 2 and Table 2). Regardless of the period over which agents were exposed to noise, there were large (tens of dB) differences for both species between the estimates of SEL made using the two different weightings (Figure 3). Although different thresholds for PTS are associated with these weightings, they do not make these weighting schemes equivalent, as measured by the proportion of the population estimated to experience PTS. This is shown in Figure 3 by the 95% prediction ellipses (the central 95% of SELs for the simulated population) in relation to their PTS thresholds.


A simulation approach to assessing environmental risk of sound exposure to marine mammals
Comparing the effect of M‐ versus A‐weightings on predicted mean SELs for two species over time—M‐weightings giving the upper curves. The horizontal lines indicate (a) Dashed lines ‐ the Southall et al. (2007) threshold for PTS in gray seals (203 dB) and harbor porpoise (215 dB) when exposed to nonpulsed sound and (b) Solid lines ‐ thresholds for PTS for use with A‐weighting. The latter are 95 dB above the threshold of hearing (Heathershaw et al., 2001), which equates to 166 dB for gray seals and 175 dB for harbor porpoise at 1 kHz. Gray shading gives a 95% prediction interval, that is, the central 95% of SELs calculated for simulated animals. Note nonlinear x‐axis for display, and sound levels are dB re 1 μPa2/s
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5383472&req=5

ece32699-fig-0003: Comparing the effect of M‐ versus A‐weightings on predicted mean SELs for two species over time—M‐weightings giving the upper curves. The horizontal lines indicate (a) Dashed lines ‐ the Southall et al. (2007) threshold for PTS in gray seals (203 dB) and harbor porpoise (215 dB) when exposed to nonpulsed sound and (b) Solid lines ‐ thresholds for PTS for use with A‐weighting. The latter are 95 dB above the threshold of hearing (Heathershaw et al., 2001), which equates to 166 dB for gray seals and 175 dB for harbor porpoise at 1 kHz. Gray shading gives a 95% prediction interval, that is, the central 95% of SELs calculated for simulated animals. Note nonlinear x‐axis for display, and sound levels are dB re 1 μPa2/s
Mentions: The choice of weighting scheme, even in combination with its associated threshold, had a marked effect on the proportion of the simulated population estimated to experience PTS (Figure 2 and Table 2). Regardless of the period over which agents were exposed to noise, there were large (tens of dB) differences for both species between the estimates of SEL made using the two different weightings (Figure 3). Although different thresholds for PTS are associated with these weightings, they do not make these weighting schemes equivalent, as measured by the proportion of the population estimated to experience PTS. This is shown in Figure 3 by the 95% prediction ellipses (the central 95% of SELs for the simulated population) in relation to their PTS thresholds.

View Article: PubMed Central - PubMed

ABSTRACT

Intense underwater sounds caused by military sonar, seismic surveys, and pile driving can harm acoustically sensitive marine mammals. Many jurisdictions require such activities to undergo marine mammal impact assessments to guide mitigation. However, the ability to assess impacts in a rigorous, quantitative way is hindered by large knowledge gaps concerning hearing ability, sensitivity, and behavioral responses to noise exposure. We describe a simulation‐based framework, called SAFESIMM (Statistical Algorithms For Estimating the Sonar Influence on Marine Megafauna), that can be used to calculate the numbers of agents (animals) likely to be affected by intense underwater sounds. We illustrate the simulation framework using two species that are likely to be affected by marine renewable energy developments in UK waters: gray seal (Halichoerus grypus) and harbor porpoise (Phocoena phocoena). We investigate three sources of uncertainty: How sound energy is perceived by agents with differing hearing abilities; how agents move in response to noise (i.e., the strength and directionality of their evasive movements); and the way in which these responses may interact with longer term constraints on agent movement. The estimate of received sound exposure level (SEL) is influenced most strongly by the weighting function used to account for the specie's presumed hearing ability. Strongly directional movement away from the sound source can cause modest reductions (~5 dB) in SEL over the short term (periods of less than 10 days). Beyond 10 days, the way in which agents respond to noise exposure has little or no effect on SEL, unless their movements are constrained by natural boundaries. Most experimental studies of noise impacts have been short‐term. However, data are needed on long‐term effects because uncertainty about predicted SELs accumulates over time. Synthesis and applications. Simulation frameworks offer a powerful way to explore, understand, and estimate effects of cumulative sound exposure on marine mammals and to quantify associated levels of uncertainty. However, they can often require subjective decisions that have important consequences for management recommendations, and the basis for these decisions must be clearly described.

No MeSH data available.