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.
Highlights
A series of high-profile strandings of beaked whales following naval sonar exercises in the late 20th century (reviewed in Jepson et al (2003)) drew public attention to the potential effects of intense anthropogenic ocean noise on marine organisms and convinced many scientists and policymakers that ocean noise is a pervasive, globally important environmental issue
We describe the statistical derivation of SAFESIMM and similar risk assessment frameworks, investigate which aspects of these frameworks are most vulnerable to knowledge gaps, and identify priority research areas
Two auditory weighting schemes are supported in the SAFESIMM: one derived from the species’ audiogram, referred to hereafter as an A-weighting (“A” for audiogram); and one derived from the M-weightings developed by Southall et al (2007)
Summary
A series of high-profile strandings of beaked whales following naval sonar exercises in the late 20th century (reviewed in Jepson et al (2003)) drew public attention to the potential effects of intense anthropogenic ocean noise on marine organisms and convinced many scientists and policymakers that ocean noise is a pervasive, globally important environmental issue. SAFESIMM and similar frameworks have been used extensively by industry and regulators to explore effects of noise-generating activities on a variety of marine mammal species, their performance has not previously been subjected to the kind of statistical scrutiny that forms the core of management strategy evaluation This requires a transparent exploration of the sensitivity of model outputs to misspecification and uncertainty in key inputs. Industrial developments that generate high-amplitude noise within important marine mammal habitats generally have to comply with country- specific policies that require an assessment of the harm likely to result from those activities These assessments may be at the individual or population level and allow managers, regulators, and decision makers to evaluate whether such levels of risk are acceptable. Managers may wish to minimize TTS or the number of behavioral disturbance events, in which case simulation approaches like SAFESIMM can be adapted to track other noise exposure metrics
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