Using dose–response functions to improve calculations of the impact of anthropogenic noise

Abstract

1. Estimating the number of animals impacted by a stressor typically involves combining a dose-response function with information about the distribution of animals and of the stressor.2. Regulators often prefer a single threshold to a full dose-response function, but much of the variability observed in the threshold at which different individuals respond to a stressor is an inherent characteristic of populations that needs to be taken into account to predict the effects of stressors. When selecting an exposure threshold, regulators need information on the proportion of the population that will be protected.3. Regulatory processes that calculate the number of animals impacted must draw from the dose-response function, the actual distribution of the animals, and a model mapping how the stressor intensity declines with distance from the source.Ignoring any of these factors can lead to significant errors in estimates of the area and numbers of animals affected.4. This paper focuses on behavioural responses of marine mammals to anthropogenic sound and demonstrates that a common approach of selecting the threshold at which half of the animals respond (RLp50) grossly underestimates the number of animals affected. We present an example, using a published dose-response function, where the number affected is underestimated by a factor of 280. Results would be similar for any stressor whose strength decreases following an inversesquare function as it dilutes into the environment.5. This paper presents a method to use a dose-response function to derive a more accurate estimate of animals affected and to set a threshold (the Effective Response Level) that corrects the problem with the RLp50 estimate.6. Estimates of effects of stressors should include estimates of uncertainty, which can be used to adapt thresholds to different policy contexts and conservation problems. ---This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Assessing the Role of Sampling Uncertainty When Predicting Behavioral Responses of Tagged Cetaceans Exposed to Naval Sonar

“…Step 1: Simulate response thresholds, t ij , for each animal i in exposure session j. • Step 2: Determine the corresponding range between whale and sonar source, under the assumption that sound spreads equally in all directions (i.e., inverse-square spherical spreading) (Tyack and Thomas, 2019). For these calculations, the absorption coefficient was set to 0.185 dB re 1µPa per km, which corresponds to the rate of absorption of an acoustic signal emitted at a nominal frequency of 3 kHz under normal sea conditions (Miller et al, 2014…”

“…However, this approach ignores the fact that responses do not necessarily scale with dose (Gomez et al, 2016), and overlooks the complexities of wildlife responsiveness to sound, including the large suite of contextual factors that drive the onset and intensity of behavioral responses observed to date both within and across taxonomic groups (Ellison et al, 2012). Tyack and Thomas (2019) recently proposed the effective response radius (or effective response range, ERR) as an alternative and unbiased diagnostic of impact for management applications. The ERR is derived from probabilistic doseresponse curves and can be combined with information on animal density to determine the number of individuals expected to respond under given exposure conditions, making it a key metric in environmental impact assessments.…”

“…Specifically, the ERR represents an "effect zone" (sensu Faulkner et al, 2018) which quantifies the distance beyond which as many animals respond as do not respond within it; it follows that the total number of animals (both those that respond and those that do not) within this range is identical to the overall number of animals responding at all distances, assuming that individuals are distributed evenly throughout the region of interest (although other spatial distributions can be readily incorporated; see Tyack and Thomas, 2019 for details). Using acoustic propagation models, the ERR can additionally be translated into an effective received level (ERL), which yields an identical estimate of impact when used as a threshold in a step function as would be obtained from a full dose-response function (Tyack and Thomas, 2019). To explore the consequences of sampling uncertainty on decision-making, we used posterior estimates of model parameters to compute the ERR in each simulation, assuming spherical spreading loss of the acoustic signals.…”

“…To address this, we explored the effects of sample size and tag choice on our ability to quantify dose-response ("risk") functions from multi-tag BRS data. In the past, managers have relied on "all-or-none" step functions to estimate the proportion of a population impacted by a sound source, but this approach can grossly underestimate risk, and reduces management criteria to a single threshold that is unlikely to be adequate for meeting different regulatory needs (Tyack and Thomas, 2019). Instead, novel statistical methods have recently emerged to quantify dose-response relationships in the form of flexible probabilistic curves (Miller et al, 2014;Moretti et al, 2014).…”

Ecological Risk Assessment of Underwater Sounds from Dredging Operations

“…In the context of underwater sound exposures, potential site‐specific information and studies that would be included in a response assessment could include collection of site‐specific sound data, refinements to exposure modeling, and biological monitoring. Additionally, long‐term research needs are continued refinements to ex situ (laboratory‐scale) and in situ (field‐scale) biological dose–response functions (Tyack and Thomas 2019).…”