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dc.contributor.authorTyack, Peter Lloyd
dc.contributor.authorThomas, Len
dc.date.accessioned2019-09-09T08:30:02Z
dc.date.available2019-09-09T08:30:02Z
dc.date.issued2019-09-06
dc.identifier258702458
dc.identifierb3c5bf54-9129-4400-b34c-df815eca3287
dc.identifier85071749652
dc.identifier000484997200017
dc.identifier.citationTyack , P L & Thomas , L 2019 , ' Using dose-response functions to improve calculations of the impact of anthropogenic noise ' , Aquatic Conservation: Marine and Freshwater Ecosystems , vol. 29 , no. S1 , pp. 242-253 . https://doi.org/10.1002/aqc.3149en
dc.identifier.issn1052-7613
dc.identifier.otherORCID: /0000-0002-7436-067X/work/61369991
dc.identifier.otherORCID: /0000-0002-8409-4790/work/61370049
dc.identifier.urihttps://hdl.handle.net/10023/18434
dc.descriptionFunding: Office of Naval Research, Grant/Award Numbers: N00014‐12‐1‐0204 and N00014‐15‐1‐2553; Scottish Funding Council, Grant/Award Number: HR09011.en
dc.description.abstract1. 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 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 under-estimated by a factor of 280. Results would be similar for any stressor whose strength decreases following an inverse-square 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.
dc.format.extent1173420
dc.language.isoeng
dc.relation.ispartofAquatic Conservation: Marine and Freshwater Ecosystemsen
dc.subjectAcoustic thresholden
dc.subjectBehavioural responseen
dc.subjectBehavioural takeen
dc.subjectDose: responseen
dc.subjectDose-responseen
dc.subjectStressor exposureen
dc.subjectGE Environmental Sciencesen
dc.subjectGC Oceanographyen
dc.subjectQH301 Biologyen
dc.subjectDASen
dc.subjectSDG 14 - Life Below Wateren
dc.subject.lccGEen
dc.subject.lccGCen
dc.subject.lccQH301en
dc.titleUsing dose-response functions to improve calculations of the impact of anthropogenic noiseen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Centre for Social Learning & Cognitive Evolutionen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.contributor.institutionUniversity of St Andrews. Sea Mammal Research Uniten
dc.contributor.institutionUniversity of St Andrews. Sound Tags Groupen
dc.contributor.institutionUniversity of St Andrews. Bioacoustics groupen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.identifier.doi10.1002/aqc.3149
dc.description.statusPeer revieweden


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