# Application of a risk-based approach to continuous underwater noise at local and subregional scales for the Marine Strategy Framework Directive

Emma Verling, Ramon Miralles Ricós, Manuel Bou-Cabo, Guillermo Lara, Maria Garagouni, Jean-Marc Brignon, Tim O'higgins

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Emma Verling, Ramon Miralles Ricós, Manuel Bou-Cabo, Guillermo Lara, Maria Garagouni, et al. Application of a risk-based approach to continuous underwater noise at local and subregional scales for the Marine Strategy Framework Directive. Marine Policy, 2021, 134, pp.104786. 10.1016/j.marpol.2021.104786. ineris-03500533

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# Application of a risk-based approach to continuous underwater noise at local and subregional scales for the Marine Strategy Framework Directive

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Emma Verling<sup>a, *</sup>, Ramon Miralles Ricós<sup>b</sup>, Manuel Bou-Cabo<sup>c, d</sup>, Guillermo Lara<sup>c, d</sup>, Maria Garagouni<sup>a, e</sup>, Jean-Marc Brignon<sup>f</sup>, Tim O'Higgins<sup>a</sup>

## ARTICLE INFO

### Keywords:

MSFD

Risk assessment

Underwater noise

Cetaceans

Ecosystem-based management

## ABSTRACT

The Marine Strategy Framework Directive's (MSFD) goal of achieving Good Environmental Status (GES) in European waters presents Member States (MS) with a significant challenge in measurement and monitoring of a suite of descriptors of GES, some of which are poorly understood. To address the burden for MSs of monitoring and measuring environmental status and trends over vast areas, provisions have been made for the use of risk-based approaches to assessment and monitoring. Here, a standardised risk-based approach is described, aligned with the articles of the directive and with the DAPSI(W)R(M) conceptual frame. This risk approach is then applied at two different spatial scales for the risks associated with continuous underwater noise (D11C2). The first case is a subregional scale application for the North East Atlantic assessing risk under data poor conditions while the second case focuses on the Bay of Biscay, using the same process but under data rich conditions. The parallel applications of the risk-based approach illustrate that it is a powerful tool that can provide useful outputs, even where significant data gaps and limitations in understanding exist. We demonstrate the adaptability and wider applicability of the risk-based approach and finally make recommendations as to how improvements in data availability and accessibility could increase the confidence in the outputs.

## 1. Introduction

The European Commission's Marine Strategy Framework Directive (MSFD) is a holistic and ambitious policy instrument with a wide geographic and environmental scope [18]. Its overarching aim of achieving Good Environmental Status (GES) in European waters presents Member States (MS) with a challenging array of considerations including monitoring (article 11), assessment (article 8), and development of programmes of measures (Articles 10, 13), and the Directive also mandates that GES be achieved on a regional sea basis, necessitating regional collaboration. The directive is underpinned by 11 "Descriptors" of GES, some of which are already reasonably well-understood, due to a large volume of research and because their development has been steered by other policy instruments over a long time period (e.g. the Common Fisheries Policy for D3 Commercial Fish and Shellfish and the

Water Framework Directive in the case of D5 Eutrophication). However, in order to encompass new and emerging pressures in the marine environment that are not explicitly tackled by other policies, a number of more novel descriptors were also included in the directive, namely D10 (Litter) and D11 (Noise). While this has bolstered the MSFD's central role in a complex marine policy landscape (e.g. [12,44,52]), the lack of knowledge associated with these descriptors has meant that even defining GES for them has represented a major challenge.

A report of the MSFD's first cycle identified a range of general shortcomings and inadequacies in the information reported, including a lack of regional harmonisation [19]. Whilst Articles 3(5) and 5 of the MSFD specifically mandate subregional determination of GES, no specific governance arrangements are defined within the directive to facilitate a regional approach [53,68]. As a result, the application of marine strategies in the first cycle occurred principally at a national

<footnote><sup>a</sup> MaREI Centre for Marine and Renewable Energy, Environmental Research Institute, University College Cork, Ireland</footnote>

<footnote><sup>b</sup> Institute of Telecommunications and Multimedia Applications (iTEAM), Universitat Politècnica de València, Spain</footnote>

<footnote><sup>c</sup> Spanish Institute of Oceanography (IEO), C.O. Murcia, C/ el Varadero 1 30740 Lo Pagan, Murcia, Spain</footnote>

<footnote><sup>d</sup> Unidad Mixta IEO - UPV, Tinglados Muelle frutero, 46730 Grau de Gandía, Valencia, Spain</footnote>

<footnote><sup>e</sup> Laboratory of Ichthyology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece</footnote>

<footnote><sup>f</sup> INERIS, Parc Technologique ALATA, BP 2, Verneuil-en-Halatte, France</footnote>

<footnote>* Corresponding author.
E-mail address: emma.verling@ucc.ie (E. Verling).</footnote>
level, making inter-comparisons of datasets or approaches very difficult [8,9] and creating a significant barrier to effective implementation (e.g. [6,9]). This apparent resistance to collaboration amongst MS may itself be driven by a lack of time, funds and expertise [7,9]. In some cases, assessments need to be made on elements for which the evidence is inconclusive, and for which data collection to improve the knowledge base is prohibitively expensive. To address this and in recognition of the significant financial burden in general for MSs of monitoring and measuring environmental status and trends over vast areas, Commission Decision 2017/848 [20] made provisions for risk-based approaches to assessment of criteria and set out a clearer, more easily targeted elaboration of the GES descriptors.

In the marine environment, acoustics represent an important means by which marine organisms communicate, forage and reproduce [65]. This century has seen an increase in the disruption of ambient ocean soundscapes by anthropogenic sound [60] and the wide-ranging impacts of underwater noise have come into focus [13,17,25,33,54,62,72]. The MSFD mandates that underwater noise (either impulsive or continuous) should not be at levels that are harmful to marine organisms. While many species are potentially affected by noise, marine mammals and in particular cetaceans are a major concern, due to both to their conservation status and their reliance on sound for echolocation and communication. However, many years of study of individual cetacean species have resulted in inconsistent and inconclusive results as to the impacts of continuous marine noise on cetaceans [40,50,63]. While the impact of intense short-term noise on a number of different taxa has been well-documented (e.g. [3,42,51,64]), there remains a lack of confidence in the impacts of long-term exposure to low-frequency (< 300 Hz) noise [45,61]. This is out of step with the pervasive and persistent nature of continuous low-frequency noise generated by commercial shipping, in particular in the northern hemisphere [10,28]. With the growing political profile of underwater noise globally (e.g. [18,30,49,66]) and the explicit citing of continuous noise as a Descriptor to be considered in the MSFD (Descriptor 11, Criterion 2), the need for a risk-based and subregional approach to the issue in Europe has never been greater. Descriptor 11 is a particularly appropriate descriptor upon which to test risk-based and regional approaches since continuous low-frequency noise can cross entire ocean basins [15]. and both its distribution and effects are often poorly constrained. Difficulties in understanding the distribution of noise pressure results from the complexity of moving sound sources and varying physical oceanographic and seabed characteristics [23]. Uncertainties relating to the environmental impacts on receptors result firstly from the fact that they are often highly motile, experiencing very different noise fields depending on their location relative to a noise source, and secondly due to uncertainties in the effects of noise on behaviour and survival [17] which make identification of areas and species at risk particularly challenging.

The inclusion in the MSFD of risk-based considerations, as well as the mandate to work at regional scales result in a requirement to develop a common understanding and best practices in applying regional risk-based methodologies, and these are beginning to emerge. Judd et al. [34] set out a 6-stage methodology for the assessment of cumulative effects in marine environments, while Cormier et al. [12] used bow-tie methodologies to assess risk in the marine environment. The standards of risk assessment developed by the International Standards Organisation (ISO) provide a robust framework for risk management [31,32]. Such risk assessment approaches have already been tested at different spatial scales for Ecosystem Based Management (EBM) systems (e.g. [57, 58]), and have been found to be useful for interpretation of data from experts, indicators and ecosystem models [4]. Sardá et al. [56] argued for a standardised approach to EBM for MSFD and practical examples of this approach can be found in [57]. Furthermore, Keith [36] found risk assessment to be an extremely valuable early step in strategic ecosystem management. However, there is as yet no practical standard methodology for application of Risk-Based Approaches to MSFD.

The objective of this paper is to develop and test a robust risk-based

methodology by bringing together the legal articles of the MSFD and a standard methodology which is based on ISO risk assessment standards and harmonised with the conceptual frame of the DAPSI(W)R(M) and its predecessors [11,14]. We illustrate this risk-based methodology through two applications of it to underwater noise with a focus on the difference in quality and availability of data at different spatial scales.

## 2. Methods: the risk approach

This section outlines how the elements of ISO risk management standards, the articles of the MSFD and the elements of the DAPSI(W)R (M) conceptual frame can be aligned to develop a risk management approach for MSFD.

The Driver Pressure State Impact Response (DPSIR) model evolved from the risk-based Stressor-Response models and its successors and culminated in the DAPSI(W)R(M) model shown in Fig. 1 (based on Elliott et al. [14]). These terms have evolved considerably over the years (e.g. [11]) and have become the standard way of thinking within the MSFD. The language of this conceptual frame is used in commission decisions, the updated directive and many staff working documents. In brief, Drivers can be conceptualised as economic sectors such as fishing, agriculture or tourism. These Drivers result in specific Activities (e.g. trawling or longlining), the resulting Pressures on the marine differ between Activities resulting in changes to environmental State (traditionally called environmental Impacts), which in turn cause positive or negative Impacts on human Welfare. To address the changes in environmental State, management Responses can be directed at any of the other elements in the system, and under the MSFD these take the form of Measures.

The International Standards Organisation (ISO) has developed standards of risk management and assessment for wide application across many different disciplines. The risk-based approach used in this study was underpinned by the concepts presented in ISO31000: Risk Management-Guidelines [32] and further developed in the supporting standard ISO31010: Risk Management – Risk Assessment Techniques [31]. It therefore draws from accepted definitions of Risk and Risk Assessment [1] as a qualitative or quantitative joint assessment of the likelihood and consequence of adverse outcomes of events or activities. Five key Risk Management phases are identified within these standards and their relation to the relevant articles in the MSFD and to the DAPSI (W)R(M) element are illustrated in Fig. 2. The remainder of this study

<figure><img src="image_4.png" /><figcaption>Fig. 1. Illustrating the DAPSI(W)R(M) model, the most recent development in the research into Driver – Pressure – State.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Showing the links between the ISO Risk phases (black) mapped against the articles of the MSFD social (blue) and ecological (green) elements of the DAPSI(W) R(M) conceptual frame. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).</figcaption></figure>

follows these five risk steps for Descriptor 11, Criterion 2 of the MSFD,
which are described below.

2.1. Step 1. Establishing the context

For the purposes of MSFD the context is established within the
directive itself, mandating regional cooperation to achieve good envi-
ronmental status in Europe's Seas. This section also lays out the pa-
rameters used to measure risk and assessment scale. Descriptor 11 is a
pressure-related descriptor and therefore requires information about
pressures but also about their consequences for the relevant receptors.

2.2. Step 2. Risk identification

Annex 3 of the Directive identifies a range of human activities which
create pressures in the environment and this is used as a basis for risk
identification in our methodology, alongside the description of the most
relevant receptors for these pressures.

2.3. Step 3. Risk analysis

There are three sub-steps within the Risk Analysis section:

• Preliminary Analysis.

• Likelihood Analysis.

• Consequence Analysis.

Following a preliminary analysis, the aim of which is to remove areas
of no or extremely low risk, comes an assessment of Likelihood. This is
achieved by overlaying the Pressures (associated with specific Activ-
ities) with their receptors (elements of environmental State). The like-
lihood (or exposure to pressures) analysis can then be used to identify
points in space or time where risk might occur.

Finally, the Consequence Analysis involves an enquiry into the
relative magnitude of environmental State changes which might be
associated with a particular level of pressure. The three steps of the risk
assessment phase may occur in parallel. For example, if a particular
species is present and known to be sensitive to a specific pressure, the
consequence analysis may guide the assessment of likelihood.

## 2.4. Step 4. Risk evaluation

In this step, the risks identified can be evaluated qualitatively or quantitatively (depending on the best available knowledge) to prioritise where management responses are required, and to explore sources of uncertainty which may be addressed through measurement and monitoring. The aim is to arrive at a measure of **relative risk**, which would in turn inform GES assessments and enable prioritisation of Measures. Following the ISO process and building on work carried out in the MISTIC SEAS II project [48], the likelihood and consequence information from Step 4 can be considered together as follows:

$$RISK = LIKELIHOOD \times CONSEQUENCE$$

## 2.5. Step 5. Risk treatment

In this phase specific measures can be directed at Activities, Pressures, environmental State or directly at Welfare, in order to reduce or mitigate the risk. For example, measures directed at activities could include a ban on certain Activities within an area (e.g. ban on shipping) while measures directed at reducing Pressure could involve engineering solutions to reduce the noise pressure levels from maritime transport. Measures directed at environmental State typically include conservation or restoration efforts, while measures directed at Welfare could in theory be used to compensate individuals for a loss in environmental status (e.g. compensation for lost whale watching revenues following a catastrophic loss of cetacean species due to a certain activity).

# 3. Results: applying the risk approach to Descriptor 11

Two parallel applications of the risk-based approach for continuous underwater noise are applied below. The subregional case study, addressing several species across the North East Atlantic is a data poor case study which combines available data, expert judgement and proxies to develop a risk-based approach. The local scale case study involved the collection of high temporal and spatial resolution data to address the effects of underwater noise on a single species *Tursiops truncatus* (Bottlenose Dolphin) in the Bay of Biscay. Table 1 summarizes the parallel steps of each analysis.

<caption>**Table 1**</caption>

ISO risk Steps 2–5 and their applications to subregional and local risk management for continuous underwater noise in the North East Atlantic.

<table><thead><tr><th rowspan="2">ISO step</th><th rowspan="2">Phase</th><th colspan="2">Continuous underwater noise – D11, C2</th></tr><tr><th>Subregional scale</th><th>Local scale</th></tr></thead><tbody><tr><td>Establishing the context</td><td></td><td>Establishing policy context, assessment scales</td><td><i>Tursiops truncatus</i> prioritised.</td></tr><tr><td>Risk Identification</td><td>Data Gathering</td><td>Identification of proxy pressure data. Identification of Cetacean data from reports; priority index development for species selection</td><td>Passive acoustic monitoring campaign- Bay of Biscay. June–September 2019.</td></tr><tr><td>Risk Analysis</td><td>Preliminary Analysis<br>Likelihood (exposure)<br>Pressure elements</td><td>Data cleaning, elimination of no- and low-risk data<br>EMODNet- AIS data 2017 (1 km × 1 km), ship hours/km²/month</td><td>A Randi model was applied over the AIS data available for 2019 accounting for seasonal variations of speed of sound in water column based on Argo data stored at EMODnet (www.emodnet.eu). These data were validated using the acoustic monitoring campaign.</td></tr><tr><td></td><td>State elements</td><td>SCANS I, II and III reports for the Celtic Seas and Bay of Biscay [26], the ObSERVE report for part of the Celtic Seas [55] and the MISTIC Seas report for Macaronesia [59]. Units individuals/km²</td><td>Cetacean data for the Bay of Biscay were obtained thanks to the IEO PELACUS campaign. In this campaign density models for different species of marine mammals were acquired for the period 2007–2017.</td></tr><tr><td></td><td>Consequence (sensitivity)</td><td>Quantitative data for cetacean's sensitivity to noise is absent or inconclusive and therefore a scoring process was designed to identify the most sensitive species, this was applied in all cases (Regional, local). Expert judgement was used to complete the scoring process</td><td></td></tr><tr><td>Risk Evaluation</td><td></td><td>Likelihood and Consequence of different species graphed to enable relative risk to be established prioritisation of specific management units</td><td>Likelihood and Consequence overlaid geographically for a single species to identify areas of highest risk</td></tr></tbody></table>

## 3.1. Step 1. Establishing the context

### 3.1.1. Policy context

The MSFD mandates that “Introduction of energy, including underwater noise, is at levels that do not adversely affect the marine environment”. As well as the MSFD itself, two further EC documents contain additional information to support this work. Firstly, the Commission Decision 2017/848 [20] sets out in detail the criteria that should be considered for each of the MSFD Descriptors and secondly, Commission Directive 2017/845 [21] lists a number of drivers, all of which could generate anthropogenic noise to a greater or lesser extent. As well as providing context, the information in these documents informs the later Risk Identification step (Step 2) by indicating a pathway between the Activities, Pressures and the sensitive receptors (State changes).

### 3.1.2. Assessment scale

In this study, we applied the risk steps at two different spatial scales as follows (and shown in Fig. 3 and Table 1):

* A subregional scale (in the MSFD context). The subregional work covered the MSFD subregions The Bay of Biscay and Iberian Shelf (hereafter referred to as The Bay of Biscay), Celtic Seas and Macaronesia. The work focussed on the EEZs of France, Ireland, Portugal and Spain within this area.

* A local scale covering a small area within the Bay of Biscay subregion only.

The remainder of this work is arranged such that each step of the risk analysis procedure is described sequentially. For each step, the regional scale analysis is first described followed by the local scale analysis for that step.

## 3.2. Step 2. Risk identification

The purpose of risk identification is to find, recognise and describe risks, by defining the relevant risk pathways.

### 3.2.1. Identifying the appropriate receptors

A number of surveys over many years have attempted to study the density and distribution of cetacean species in European waters. For the subregional scale work, density data collected during these dedicated vessel and aerial surveys were extracted from relevant reports, SCANS I,
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Showing the MSFD sub-regions included in the study, as well as the survey boxes from which cetacean density data were obtained. This study focussed on the EEZs of Ireland, France, Spain and Portugal.</figcaption></figure>

II and III and ObSERVE reports for the Celtic Seas and Bay of Biscay [26, 55] and the MISTIC Seas report and for Macaronesia [59]. These data represent the most up-to-date density information for cetacean species within the study area, and most were collected during 2015 and 2016. In total the survey reports gathered sufficient data to estimate densities for 16 species in the study region. While some species cross geographic boundaries within the study area, others have more discrete distributions and consideration of GES in different regions would feature varying combinations of these species. However, all were considered in this study. In order to identify which species were most impacted by noise and warranted further assessment for Descriptor 11: Criterion 2, a priority index involving five binary (yes/no) questions was developed (see Table 2 for the questions and answers for each of the 16 species; a species needed to have a ‘yes’ answer for all five questions to be included in this long list). This Priority Index process generated a ‘long list’ of 13 receptor species which can be further refined during the later Risk Analysis step.

For the local level work, modelled densities in the Bay of Biscay for different cetacean species based on the IEO PELACUS campaign were acquired for the period 2007–2017 [29,59]. The density models were created using ‘distance sampling’ and a modified General Additive Modelling technique [59]. Though the priority index above was not formally applied, the Bottlenose Dolphin (*Trusiops truncatus*) was

selected as a target species for analysis due to its ability to detect low frequency noise, impacts from noise, high local abundance and representativeness (as in Table 2 above). The absence of established thresholds in the directive makes it impossible to establish a direct relationship between ship traffic noise and a particular Sound Pressure Level (SPL) representing Good Environmental Status. The specific risk identified for *T. truncatus* therefore was that of acoustic masking: the disruption of the species communication activities due to anthropogenic sounds. The approach to studying masking depends on the acoustic variable used, and the information retrieved can be interpreted in different ways (see Erbe et al. [16] for a review of auditory masking in cetaceans). In this case masking was assessed based on the Communication Distance Reduction (CDR; see Fig. 4 for an illustration). The Communication Distance is defined as the distance at which the ratio of (ship) noise and (Cetacean) signal arriving at an ideal listener have the same value (i.e., when the ship noise is equal to the signal omitted by a dolphin at a particular point). The difference between the ambient Communication Distance in the environment and that expected in pristine ambient conditions was used as an indicator for the risk analysis (see Verling et al. [69] for a detailed technical description).

### 3.2.2. Identifying pressure information

Having established the species which are susceptible to continuous
<caption>**Table 2**</caption>

Cetacean species and questions used to generate priority index. Impacts can include Physical, auditory and non-auditory impacts, impacts on perception, and behaviour indirect impacts and chronic impacts related to stress, disease viability and reproductive success. A species that returned a 'no' result for any question was eliminated from further investigation.

<table><thead><tr><th>Species</th><th>Q1<br>Can the species detect noise</th><th>Q2<br>Is the species impacted by noise?</th><th>Q3<br>Are distribution density data available</th><th>Q4<br>Is the species representative of a species group or taxon?</th><th>Q5<br>is the species abundant or common in the area?</th></tr></thead><tbody><tr><td>White Beaked Dolphin</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>White Sided Dolphin</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>Minke Whale</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Bottle Nosed Dolphin</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Harbour Porpoise</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Risso's Dolphin</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Long-finned Pilot Whale</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sperm Whale</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Beaked Whales</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Striped Dolphin</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Common Dolphin</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Fin Whale</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Spotted Dolphin</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Short-finned Pilot Whale</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>Sowerbys Beaked Whale</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Baleen Whales</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Diagram illustrating the distance reduction concept.</figcaption></figure>

underwater noise in the areas of interest, the Risk Identification step focussed on finding sources of information about the distribution of noise pressure in order to enable assessment of exposure. Two major considerations were relevant at this stage, quality and availability of data. Ideally direct quantitative measurements based on acoustic surveys can be used to assess sound pressure levels in a particular location, and while there have been some large-scale acoustic surveys, they are limited in geographic scale (e.g. the ObSERVE Programme [55]). Validated models based on direct observations offer a more cost-effective means of estimating sound pressure levels in a given location, but no large-scale sound pressure level models have been developed that cover the entire area of interest, and those models which have been developed (e.g. [24]) are not freely available. An alternative approach is the use of proxies, where the spatial distribution of an activity is inferred to relate to the spatial distributions of their resulting pressures. In the absence of more reliable data sources at the appropriate spatial scales, ship traffic data therefore offer a lower cost (and lower confidence) alternative to

assessing the approximate distribution of pressure.

At the subregional scale, the European Marine Observation and Data Network, EMODnet (www.emodnet-humanactivities.eu) has developed a processed shipping dataset using AIS data for European waters, currently covering 2017–2019. At present, this represents the only freely available dataset that can provide shipping information at the appropriate scale to inform studies of continuous underwater noise. The dataset expresses levels of shipping activity as ship hours per square kilometre per month with a resolution of 1 km. full details of the data processing used to derive the dataset can be found in Falco et al. [22] This dataset was used in this study as a proxy for the distribution of underwater noise for the subregional scale analysis.

The approach to risk assessment at a local scale in the Bay of Biscay was the same as that used at a subregional scale with continuous noise emitted by ship traffic also being the focus. However, in this case a modelled pressure map (as opposed to a proxy layer) reflecting ship noise was created. An annual AIS ship traffic dataset for the area was obtained, which contained information on speed over ground, length and tonnage of each ship. Ships were treated as acoustic sources by applying a Randi Model [2] for 10 kHz frequency. In addition, seasonal variations of speed of sound in the water column were considered using Argo data (www.emodnet.eu) and the MacKenzie equation [41] was used to combine sound velocity with temperature, salinity and depth. Using all of these information sources, a noise model of the area was created and later validated with data from a passive acoustic monitoring campaign. Recordings were performed using SAMARUC passive acoustic monitoring device [38,46,47] in the Bay of Biscay (N43°36,682; W02°39,419) from June 20th, 2019 to September 20th, 2019, during which 25 days of acoustic data were collected. Ambient noise indicators of 1/3 octave at different frequencies (63 Hz, 125 Hz, 2 kHz and 5 kHz) were obtained [38] to assess the accuracy of the model. The type of exposure considered was based on the Communication Distance Reduction for this species at 1 kHz, 5 kHz and 10 kHz frequencies. These specific frequency bands were selected due to their overlapping with the sound repertoire of the *T. truncatus*, which ranges from 0.8 kHz to 24 kHz [5,39,70].
### 3.3. Step 3. Risk analysis

#### 3.3.1. Risk analysis – subregional scale

The first step of the risk analysis was a preliminary analysis, the aim of which is to exclude areas or situations where risk is very low or non-existent (see ISO [31]), which can then be eliminated from further examination. The EMODnet shipping dataset for 2017 clipped to the study area totalled 3,064,473 square km, representing the EEZs of Ireland, France, Spain and Portugal. As regards shipping density, 145,485 square km of the total area were found to have greater than one ship hour per month. Fig. 5(a) shows that the majority of the grid cells with greater than 1 ship hour were located close to the coast and analysis showed that 74% were within 50 km of the coast. Although Macaronesia was the largest region by area, it had the smallest area (7910 square km) with a shipping density of greater than one ship hour (see Fig. 5(b)). By contrast, the Bay of Biscay had the highest area (107,790 square km) in which there was greater than one ship hour. Following the preliminary analysis, 2,918,988 square km with fewer than one ship hour were excluded from further analysis and only the area with greater than one ship hour per month were analysed further.

The next stage of the risk analysis involved a likelihood and consequence analysis (see ISO [31]) on the remaining area of 145,485 square km. The likelihood analysis examined the overlap between the Pressure and the ecosystem element (i.e. the exposure).

#### 3.3.2. Exposure/likelihood

To allow the cetacean density and shipping density to be examined together, the average density per cetacean species (those listed in Table 2) within each survey box was obtained from the relevant cetacean data source [26,55,59] (See Fig. 2). The average shipping density (number of ship hours per month) was also obtained for survey boxes using the Zonal Statistics function in ArcGIS. To identify areas where cetacean densities and shipping densities were high, the average cetacean densities were then overlain with the average shipping densities for each survey box (see Fig. 6). A likelihood score per cetacean species per survey box was then calculated. This was done by obtaining a proportional density (relative to the whole study area) per species per survey box and multiplying this by the shipping density for this survey box. This score can be used in the risk evaluation step along with a score for consequence, which together will be used to establish risk level.

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Preliminary analysis of EMODnet shipping density data in the study area, showing (a) where the number of ship hours per month was < 1 (hatched area) and > 1 (purple area) and (b) each subregion represented graphically, showing the relative number of square kilometres where the number of ship hour was < 1 (hatched) and > 1 (purple). Only the purple areas were taken forward for further analysis. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).</figcaption></figure>

#### 3.3.3. Consequence/sensitivity

Having established relative levels of exposure of Cetaceans to shipping activity, the aim of the consequence analysis was to assess the risk of adverse consequences from exposure of the cetacean populations (identified in step 1). Data describing cetacean's sensitivity to ship noise remains inconclusive (see Erbe et al. [16] and Southall et al. [62]) therefore the consequence analysis was conducted by performing a qualitative expert-judgement-based scoring process, designed to identify the species likely to be most affected by exposure (Box 1). Consequence analysis was applied to eight species, based on the inputs of a total of three experts. Where more than one expert completed the process, the mean of their scores was used. These species were chosen to illustrate how the process could be applied to a range of species with differing ecologies and geographical distributions. Table 3 illustrates the results of the sensitivity analysis and likelihood scores.

#### 3.3.4. Risk analysis – local level

The availability of quantitative information on both cetacean species abundance and sound pressure levels enabled a fully quantitative spatial analysis of risk to a particular species from a particular effect, auditory masking. The likelihood analysis used modelled noise pressure level maps at specific frequencies to consider the risk of masking to a single target receptor species based on Communication Distance Reduction (CDR; see Section 3.1 above for further detail). This was achieved by calculating the distance where animals have a sound excess equal to the signal to noise ratio under two scenarios:

* natural ambient noise (pristine ambient) only, and

* including the ship noise present in the area.

This comparison of these two values allowed the percentage CDR due to maritime traffic to be determined. In this approach, masking is included as a concept and no threshold levels are required to identify areas where masking is greater.

The assumption was made that whatever the associated noise level was, it would generate a masking effect depending on the distance between emitter and receiver. By applying sonar equations, the distance from the emitter to the receiver was calculated, so that they receive at least the same SPL from social calls (whistles, moans, etc) when there is acoustic noise present in the area under consideration. The results of this are illustrated in Fig. 7.

<figure><img src="image_9.png" /></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Graph of cetacean species' exposure to ship traffic for the subregional analysis, shapes indicate the survey origins of the data, colours indicate the location of the cetaceans.</figcaption></figure>

## Box 1

### Sensitivity analysis

The consequence of the noise pressure for each population was quantified by adding up a list of scaled (from 0 to 4) criteria, so that a higher total value indicated more severe consequences of noise disturbance. The criteria used were:

1. **Conservation status.** This was based on the IUCN Red List classification, but also took into account regional directives (e.g., the EU Habitats Directive Annex II/IV) and local population assessments. CR/EN = 4, DD/VU = 3, NT = 2, LC = 0, Under EU protection = 3, Locally threatened = 4.

2. **Critical/important habitats affected.** These included areas such as foraging hotspots, breeding/nursing grounds, and migration corridors, as well as areas that encompassed all or most of a species' known range. None = 0, ~ one third of breeding grounds = 3, More than half the feeding grounds = 4.

3. **Sensitive life stage affected.** This took into account that impacts on reproduction and survival of juveniles to reproductive age will negatively affect a population's ability to recover from disturbance, thus lowering its resilience. None = 0, Neonates/Pregnant females = 4.

4. **Type and severity of sensitivity to acoustic disturbance.** This criteria weighted impact by the probability of occurrence and the severity of the outcome, i.e., how commonly is that effect observed and how damaging are the consequences. The two values were then multiplied to give the overall sensitivity for that species. The following table illustrates this:

<table><thead><tr><th rowspan="2">Type of impact</th><th>Probability</th><th>Severity</th><th>Sensitivity</th></tr><tr><th>Uncertain/High/Med/Low</th><th>Uncertain/High/Med/Low</th><th>Probability * Severity</th></tr></thead><tbody><tr><td>Physical (auditory)</td><td>0–1</td><td>0–1</td><td>0–1</td></tr><tr><td>Physical (non-auditory)</td><td>0–1</td><td>0–1</td><td>0–1</td></tr><tr><td>Perceptual</td><td>0–1</td><td>0–1</td><td>0–1</td></tr><tr><td>Behavioural</td><td>0–1</td><td>0–1</td><td>0–1</td></tr><tr><td colspan="3">Overall sensitivity 0–4</td><td></td></tr></tbody></table>
<table><caption>**Table 3**
Sensitivity scores for priority cetacean species calculated according to Box 1, and mean likelihood scores per species (cetacean density/shipping density).</caption><thead><tr><th rowspan="2">Species</th><th colspan="5">Sensitivity scores</th><th colspan="3">Likelihood score</th></tr><tr><th>Q1</th><th>Q2</th><th>Q3</th><th>Q4</th><th>Final score</th><th>Ship density</th><th>Species density</th><th>Final score</th></tr></thead><tbody><tr><td>Bottlenose Dolphin</td><td>0</td><td>4</td><td>3</td><td>1.75</td><td>8.75</td><td>1.94</td><td>0.28</td><td>31.61</td></tr><tr><td>Beaked Whales</td><td>4</td><td>3</td><td>4</td><td>2.56</td><td>13.56</td><td>0.59</td><td>0.04</td><td>156.04</td></tr><tr><td>Common Dolphin</td><td>1</td><td>2</td><td>4</td><td>0.5</td><td>7.5</td><td>1.61</td><td>0.94</td><td>1.61</td></tr><tr><td>Fin Whale</td><td>3</td><td>3</td><td>2</td><td>1.75</td><td>9.75</td><td>0.14</td><td>0.04</td><td>13.20</td></tr><tr><td>Minke Whale</td><td>1</td><td>2</td><td>4</td><td>0.5</td><td>7.5</td><td>0.96</td><td>0.03</td><td>339.61</td></tr><tr><td>Pilot Whale</td><td>0.5</td><td>1.25</td><td>2.13</td><td>0.56</td><td>4.44</td><td>0.75</td><td>0.04</td><td>89.06</td></tr><tr><td>Risso's Dolphin</td><td>1.5</td><td>3.25</td><td>0</td><td>0</td><td>4.75</td><td>1.68</td><td>0.02</td><td>109.60</td></tr><tr><td>Sperm Whale</td><td>3</td><td>2</td><td>2.75</td><td>0.73</td><td>9.48</td><td>0.31</td><td>0.03</td><td>18.34</td></tr></tbody></table>

<figure><img src="image_11.png" /><figcaption>**Fig. 7.** Percentage of distance reduction from pristine ambient for 2019 assuming a sea state of 1 for 1 kHz. Maximum reduction observed at this frequency was 82.6% and 16.6% was the minimum.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Fig. 8.** Risk evaluation – consequence based on sensitivity analysis and likelihood based on average exposure scores (per survey box) for each of eight species.</figcaption></figure>
Similar likelihood maps can be generated for other species by
modelling the Sound Pressure Level and CDR at the specific frequency
bands of auditory communication for other cetacean species, where
known.

As regards consequence analysis, the ecological consequences of masking in terms of decreased survival of the *T. truncatus* are currently unknown and therefore assessment of consequence relies (as in the subregional example) on the expert judgement approach (Box 1).

3.4. Step 4. Risk evaluation

3.4.1. Risk evaluation - subregional scale

In order to assess the relative risk within the different sub-regions, a mean likelihood score and consequence score (i.e. overall sensitivity score) was calculated for each species and these were considered simultaneously by representing them graphically (see Fig. 8). This graphic representation could allow relative risk to be determined in the future once Threshold Values are in place. A similar graphic technique was employed in work carried out by the MISTIC SEAS project [48], in which approaches adopted by a number of previous projects are also reviewed and see also Knights et al. [37] for related graphic approaches.

3.4.2. Risk evaluation - local scale

Despite an abundant literature studying the hearing function of marine mammals, and specifically of the Bottlenose Dolphin (*T. truncatus*) [16], it is difficult to predict if animals display masking at particular sound levels. In order to manage the possible threat of masking in certain marine areas, the risk evaluation step was undertaken based on both CDR and SPL. In both cases, normalised cumulative probability density functions were used but the results of each analysis treated risk in a different way. When SPL is used, risk evaluation is obtained by taking the product of the two normalised variables in the region. Thus, high risk means regions with *both* high density of the target

species and high SPL. When CDR is used, the risk evaluation is computed
as the product of the normalised density of the species by the CDR. As a
result, high risk in this case means regions with high density where a
large deviation from pristine ambient exists.

SPL correlated with density of *T. truncatus* allowed areas that had a relatively high population of dolphins and also high relative values of SPL to be identified. The pseudo colour map showing “risk” zones in Fig. 9 highlights those areas where SPL is relatively high and where density of *T. truncatus* is significant. However, while it might be possible to identify areas of higher risk using this method, without threshold levels masking effects cannot be assessed. The use of CDR however, allowed the masking effect over whistles and other social calls at frequencies ≤ 10 kHz to be examined. To determine how degraded the medium was with respect to noise pollution affecting cetaceans, the CDR was correlated with density of the *T. truncatus* population; the hypothesis does not consider the detection threshold or audition weight function. As expected, the % CDR with respect to pristine ambient was greater for 1 kHz due to highest level of ship noise present in the area. This was reflected in the risk maps (Fig. 9), which showed a higher value of calculated risk index at 1 kHz compared with 5 kHz and 10 kHz.

The calculation of CDR depends on the total noise in the area, making it possible to study the percentage CDR in ambient noise with or without ship traffic, which allows a comparison to be made between pristine ambient and the current situation. Cases illustrated in this work were developed assuming ambient noise for 1 kHz, 5 kHz and 10 kHz and sea state 1 (following the Beaufort Scale where wind speed is 1–3 knots and wave height is 10 cm) and using tabulated values for Wenz curves [71]. As expected, the % CDR with respect to pristine ambient decreased with increasing frequency, due to the level of ship noise present in the area. At 1 kHz, maximum values of CDR were 82.7%, at 5 kHz, the maximum value of CDR was 64.5%, and for 10 kHz, the maximum value of CDR was 55.2%. In all cases, *T. truncatus* density was 2.82 and the Risk Index was 1; see Table 4 for detailed statistics.

<figure><img src="image_13.png" /><figcaption>**Fig. 9.** Risk maps (where Risk Index = 1) based on %CDR from pristine ambient and density of *T. truncatus* for (a) 1 kHz (82.7% & 2.82% respectively), (b) 5 kHz (64.5% and 2.82% respectively) and (c) 10 kHz (55.2% and 2.82% respectively).</figcaption></figure>
<table><caption>**Table 4**
Details of statistics for distance reduction percentage at Sea State 1 at different frequencies considered.</caption><thead><tr><td>Frequency [kHz]</td><td>Mean % CDR (%)</td><td>Standard deviation (%)</td><td>Maximum (%)</td><td>Minimum (%)</td></tr></thead><tbody><tr><td>1</td><td>63.6</td><td>10.5</td><td>82.7</td><td>16.6</td></tr><tr><td>5</td><td>49.2</td><td>7.7</td><td>64.5</td><td>16.7</td></tr><tr><td>10</td><td>42.1</td><td>6.5</td><td>55.2</td><td>17.7</td></tr></tbody></table>

## 4. Discussion

The risk-based methodology described above sets out a series of standardised steps for the assessment of environmental risk and applies it to single criterion of GES. The contrasting data-poor sub-regional and data-rich local case studies provide evidence for the effectiveness and flexibility of the standardised approach, aligning the ISO risk framework with the articles of the MSFD and the information categories of the DAPSI(W)R(M). The flexibility afforded by the combination of empirical data and expert judgement is well suited to the MSFD with its 11 diverse descriptors and large geographic scale of implementation. MS EEZ territories and financial budgets vary widely, with some small MS having very large marine territories resulting in an unequal burden of implementation and enforcement. As such, the risk-based approach demonstrated here can provide a level playing field for implementation. This systematic approach to identifying and addressing the most important pressures under differing constraints offers the potential to provide a flexible common framework for assessment, across which the European Commission can fairly compare efforts made in achieving GES for the MSFD.

Full regional harmonisation would require such an approach to be agreed across EU member states and the flexibility of the approach described here means that there is not always clarity on which specific aspects of the analysis should be contained within each risk step. To this end, an agreed regional position on the distinction between steps could aid harmonised implementation. Harnessing the use of existing EU and Regional management structures to centrally apply expert judgement processes (such as the priority index described here), and to harmonise these approaches could represent an improvement in efficiency. Once further development of the RBA to MSFD takes place, future work should address Step 5 (Risk Treatment), which was not the focus of this study. Political, scientific and economic considerations will all be important if MS are to balance the requirements of global trade with the need to protect cetacean species. However, there are examples internationally which may be able to inform and guide this process (e.g. [35]).

For Underwater Noise, the potential for MSFD to achieve its goal of GES is undermined by lack of data and empirical evidence on impacts to support decisions. In the subregional analysis, the absence of pressure data remains an obstacle to effective risk assessment. Our approach, taking shipping density as a proxy in the absence of better data may underestimate the spatial scales of the risk caused by underwater noise pressures. However, continued developments in the science of noise modelling (e.g. via the SATURN project; https://www.saturnh2020.eu/) also increases our understanding of ocean soundscapes and improves the knowledge base for the use of risk-based approaches. Once continuous noise maps are fully developed, a systematic approach to identifying the physical characteristics of locations where shipping density can act as a viable proxy for noise pressure should be agreed in order to improve confidence in the use of suitable proxies and efficiency of assessments. Furthermore, there is potential to more effectively integrate existing centralised spatial data collection efforts to better align them with the policy needs of MS. Acquiring shipping data such as AIS data over large areas is costly and developing an agreed approach to centralising the data collection efforts for application in MSFD could represent a considerable improvement in efficiency. The EMODnet shipping density data are centralised and free of charge, with data available for a variety

of vessel types at a monthly scale for a number of years. McKenna et al. [43] analysed the complexity of noise generated by different types of vessels, however at present the vessel categories used in EMODnet cannot be associated with noise profiles. In future, harmonising the ship types identified within this dataset with the categories of ships used to generate noise models could enable more effective and centralised assessment of noise pressures.

Similarly joined-up approaches to the collection, harmonisation, and archiving of cetacean density could enable more effective regional assessment. While repeat surveys would go a long way towards understanding the complexity of Cetacean population distributions, the current data represent only a snapshot in time. Concerted efforts to enable further regional cooperation in data gathering and analysis are needed, as well as the spatial data infrastructure to enable continued harmonisation of regional approaches. All of these recommendations are supported by the stipulations of the Aarhus Convention on Access to Information, Public Participation and Access to Justice in Environmental Matters [67], for which data sharing, data archive centres and freely available environmental data are central pillars, and to which the EU are contracting parties.

While our regional scale analysis was capable of identifying the relative levels of risk to species at broad spatial scales (with relatively low levels of confidence) the local scale analysis involving noise modelling provide more detailed, quantitative information about which areas are most at risk. In our local scale analysis, the risk-based methodology used two indicators of sound pressure (SPL and CDR) and these produced similar results in terms of reduction of noise effect with frequency and in terms of locating those areas where acoustic masking could occur. While both SPL and CDR can discriminate those areas where there is a risk of masking, the population level effects of such masking remain unknown and this a major obstacle in establishing ecologically based thresholds for the assessment of GES.

To empirically address these uncertainties, passive acoustic monitoring campaigns and measuring stations alongside automatic detection algorithms of cetaceans could also help to validate density models and create sensitivity maps for species. Repeated studies could track areas of increasing or decreasing risk by identifying changes in cetacean population density, migration to other zones as well changes to ship traffic volume. In reality, empirical data on noise impacts on cetaceans remain some way off, and MS must in the meantime proceed with implementing the MSFD using the best available data and methods.

Expert judgement approaches to the assessment of sensitivity, when properly conducted and recorded, is a legitimate and immediately available way to address the uncertainty of the ecological impacts of noise on cetaceans. Moreover, given the uncertainties associated with the propagation of sound underwater, and particularly the limitations in the accuracy of predicting the exact location of a receptor at a given time, it may not always be necessary to model the exact level of sound pressure at all locations. Over vast areas such as the North East Atlantic, minor variations in relative location of pressure and receptor have major consequences for the levels of exposure (See Hazelwood and Connelly [27] for an example). Under such conditions, assessing relative risk levels using vessel location as a proxy for pressure may be justified on the basis that even with entirely accurate pressure maps, exposure level cannot be accurately assessed. As the MSFD continues its second 6-year cycle, workable solutions like this must be found to enable MS to meet their obligations, particularly in cases where information is limited, and uncertainty prevails.

### 4.1. Conclusion

The ISO standard methodology provides an ideal framework within which to nest regional and subregional comparisons not just for directives like the MSFD, but for marine ecosystems more broadly. It enables regional cooperation even where data availability is patchy or non-existent and it can be used to identify knowledge gaps and steer
capacity-building efforts in appropriate directions. For Descriptor 11, further work and development is needed to make best use of the benefits of the risk-based approach. A European-scale map of maritime transport (including offshore shipping) that can be accurately related to noise is required in order to improve the accuracy and utility of risk-based approach, and to this end alignment of the shipping categories in the EMODnet datasets with those used for the modelling of underwater noise might be a useful first step. A harmonised database of cetacean density distribution is also needed; one that is spatially standardised, freely available, accompanied by appropriate metadata and crucially, that allows for temporal considerations to be taken into account. The latter is particularly important for migrating cetacean species such as the Fin Whale, for which risk levels will vary at different periods throughout the year. While work to understand the impact of noise on cetacean (and other) species will no doubt continue, this will take considerable time and resources, and in the meantime an expert judgement-led sensitivity process provides a realistic and effective solution.

### CRediT authorship contribution statement

**Emma Verling:** Conceptualization, Methodology, Investigation, Formal analysis, Data curation, Visualisation, Writing – original draft. **Ramón Miralles:** Conceptualization, Methodology, Investigation, Formal analysis, Writing – review & editing. **Manuel Bou-Cabo:** Methodology, Investigation, Formal analysis, Writing – review & editing. **Guillermo Lara:** Investigation, Formal analysis. **Maria Garagouni:** Methodology, Formal analysis, Writing – review & editing. **Jean-Marc Brignon:** Conceptualization, Methodology, Writing – review & editing. **Tim O'Higgins:** Conceptualization, Methodology, Visualisation, Writing – review & editing, Project administration, Funding acquisition.

### Acknowledgements

This work was supported by the Directorate-General Environment of the European Commission (Grant no. 110661/2018/794607/SUB/ENV. C2). We are very grateful to all partners of the RAGES (Risk Based Approaches to Good Environmental Status) consortium for discussions during the development of this work. We are grateful for the contributions of Paulus Jacobus Wensveen and Rocio Prieto Gonzalez to the cetacean sensitivity process.

### References

[1] T. Aven, Quantitative Risk Assessment: The Scientific Platform, Cambridge University Press, 2011.

[2] J.E. Breeding, L.A. Plug, E.L. Bradley, M.H. Walrod, W. McBride, Ambient Noise Directionality (RANDI) 3.1 – Physics Description (Technical Report NRL/FR/7176-95-9628), Naval Research Laboratory, 1996.

[3] H.B. Blair, N.D. Merchant, A.S. Friedlaender, D.N. Wiley, S.E. Parks, Evidence for ship noise impacts on humpback whale foraging behaviour, Biol. Lett. 12 (2016), 20160005, https://doi.org/10.1098/rsbl.2016.0005.

[4] L.M. Bland, K.E. Watermeyer, D.A. Keith, E. Nicholson, T.J. Regan, L.J. Shannon, Assessing risks to marine ecosystems with indicators, ecosystem models and experts, Biol. Conserv. 227 (2018) 19–28, https://doi.org/10.1016/j.biocon.2018.08.019.

[5] M.C. Caldwell, D.K. Caldwell, P.L. Tyack, A review of the signature-whistle-hypothesis for the Atlantic bottlenose dolphin, in: S. Leatherwood, R.R. Reeves (Eds.), The Bottlenose Dolphin, Academic Press, San Diego, 1990, pp. 199–234.

[6] M. Cavallo, M. Elliott, J. Touza, V. Quintino, The ability of regional coordination and policy integration to produce coherent marine management: implementing the Marine Strategy Framework Directive in the North-East Atlantic, Mar. Policy 68 (2016) 108–116, https://doi.org/10.1016/j.marpol.2016.02.013.

[7] M. Cavallo, M. Elliott, J. Touza, V. Quintino, Benefits and impediments for the integrated and coordinated management of European seas, Mar. Policy 86 (2017) 206–213, https://doi.org/10.1016/j.marpol.2017.09.035.

[8] M. Cavallo, M. Elliott, V. Quintino, J. Touza, Can national management measures achieve good status across international boundaries? – a case study of the Bay of Biscay and the Iberian coast sub-region. Ocean Coast. Manag. 160 (2018) 93–102, https://doi.org/10.1016/j.ocecoaman.2018.04.005.

[9] M. Cavallo, A. Borja, M. Elliott, V. Quintino, J. Touza, Impediments to achieving integrated marine management across borders: the case of the EU Marine Strategy Framework Directive, Mar. Policy 103 (2019) 68–73, https://doi.org/10.1016/j.marpol.2019.02.033.

[10] S. Cominielli, R. Devillers, H. Yurk, A.O. Macgillivray, L.H. Mcwhinnie, R. Canessa, Noise exposure from commercial shipping for the southern resident killer whale population, Mar. Pollut. Bull. 136 (2018) 177–200, https://doi.org/10.1016/j.marpolbul.2018.08.050.

[11] P. Cooper, Socio-ecological accounting: DPSWR, a modified DPSIR framework, and its application to marine ecosystems, Ecol. Econ. 94 (2013) 106–115, https://doi.org/10.1016/j.ecolecon.2013.07.010.

[12] R. Cormier, M. Elliott, A. Kannen, IEC/ISO bow-tie analysis of marine legislation: a case study of the Marine Strategy Framework Directive, ICES Coop. Res. Rep. No. 342 (2018), https://doi.org/10.17895/ices.pub.4504.

[13] C.M. Duarte, et al., The soundscape of the Anthropocene ocean, Science 371 (6529) (2021), https://doi.org/10.1126/science.aba4658.

[14] M. Elliott, D. Burdon, J.P. Atkins, A. Borja, V. Cormier, N. de Jone, R.K. Turner, "And DPSIR begat DAPSI(W)R(M)"! – a unifying framework for marine environmental management, Mar. Pollut. Bull. 118 (2017) 27–40, https://doi.org/10.1016/j.marpolbul.2017.03.049.

[15] C. Erbe, International regulation of underwater noise, Acoust. Aust. 41 (2013) 12–19.

[16] C. Erbe, C. Reichmuth, K. Cunningham, K. Lucke, R. Dooling, Communication masking in marine mammals: a review and research strategy, Mar. Pollut. Bull. 103 (2016) 15–38, https://doi.org/10.1016/j.marpolbul.2015.12.007.

[17] C. Erbe, M. Dähne, J. Gordon, H. Herata, D.S. Houser, S. Koschinski, R. Leaper, R. McCauley, B. Miller, M. Müller, A. Murray, J.N. Oswald, A.R. Scholik-Schlomer, M. Schuster, I.C. Van Opzeeland, V.M. Janik, Managing the effects of noise from ship traffic, seismic surveying and construction on marine mammals in Antarctica, Front. Mar. Sci. 6 (2019) 647, https://doi.org/10.3389/fmsar.2019.00647.

[18] European Commission, Directive 2008/56/EC of the European Parliament and of the Council of 17 June 2008 establishing a framework for community action in the field of marine environmental policy (Marine Strategy Framework Directive), Off. J. Eur. Union L 164 (2008) 19–40.

[19] European Commission, Report from the Commission to the Council and the European Parliament. First Phase of Implementation of the Marine Strategy Framework Directive (COM/2014/097), 2014.

[20] European Commission, Commission Decision 2017/848 of 17 May 2017 laying down criteria and methodological standards on good environmental status of marine waters and specifications and standardised methods for monitoring and assessment, and repealing Decision 2010/477/EU, Off. J. Eur. Union L 125 (2017) 43–74.

[21] European Commission, Commission Directive 2017/845. Amending Directive 2008/56/EC of the European Parliament and of the Council as regards the indicative lists of elements to be taken into account for the preparation of marine strategies, Off. J. Eur. Union L 125 (2017) 27–33.

[22] Falco L., Pititto A., Adnams W., Earwaker N., Greidanus H., EU Vessel Density Map Detailed Method, Joint Research Centre, Cogea and Lovell Johns, 2019. https://www.emodnet-humanactivities.eu/documents/Vessel%20density%20maps_me-thod_v1.5.pdf.

[23] A. Farcas, P.M. Thompson, N.D. Merchant, Underwater noise modelling for environmental impact assessment, Environ. Impact Assess. Rev. 57 (2016) 114–122, https://doi.org/10.1016/j.eiar.2015.11.012.

[24] A. Farcas, C.F. Powell, K.L. Brookes, N.D. Merchant, Validated shipping noise maps of the Northeast Atlantic, Sci. Total Environ. 735 (2020), 139509, https://doi.org/10.1016/j.scitotenv.2020.139509.

[25] C. Gomez, J.W. Lawson, A.J. Wright, A.D. Buren, D. Tollit, V. Lesage, A systematic review on the behavioural responses of wild marine mammals to noise: the disparity between science and policy, Can. J. Zool. 94 (2016) 801–819, https://doi.org/10.1139/cjz-2016-0098.

[26] Hammond P.S., Lacey C., Gilles A., Viquerat S., Börjesson P., Herr H., Macleod K., Ridoux V., Santos M.B., Scheidat M., Teilmann J., Vingada J., Oien N., Estimates of cetacean abundance in European Atlantic waters in summer 2016 from the SCANS-III aerial and shipboard surveys, 2017. https://synergy.st-andrews.ac.uk/scans3/files/2017/05/SCANS-III-design-based-estimates-2017-05-12-final-revised.pdf.

[27] R.A. Hazelwood, J. Connelly, Estimation of underwater noise – a simplified method, Int. J. Soc. Underw. Technol. 26 (3) (2005) 51–57, https://doi.org/10.3723/175605045784426664.

[28] J.A. Hildebrand, Anthropogenic and natural sources of ambient noise in the ocean, Mar. Ecol. Prog. Ser. 395 (2009) 5–20, https://doi.org/10.3354/meps08353.

[29] IEO PELACUS, 2017. http://www.ieo.es/en_US/web/ieo/campanas?pp_id=ieocampanasmain_WAR_IEOCampanasportlet&pp_lifecycle=0&pp_p_state=normal&pp_p_mode view&pp_p_col_id=column-2&pp_p_col_pos=1&pp_p_col_count=2&ieocampanasmain_WAR_IEOCampanasportlet_filtroNombre=pelacus_&ieocampanasmain_WAR_IEOCampanasportlet_filtroBarco=1.

[30] IMO. Guidelines for the Reduction of Underwater Noise from Commercial Shipping to Address Adverse Impacts on Marine Life (IMO MEPC.1/Circ.833), International Maritime Organisation, London, UK, 2014.

[31] ISO. International Standard. Risk Management – Risk Assessment Techniques. Edition 1.0 2009–11, International Standards Organisation, Geneva, 2009.

[32] ISO. International Standard. Risk Management – Guidelines. Second Edition 2018-02, International Standards Organisation, Geneva, 2018.

[33] N. Jones, The quest for quieter seas, Nature 568 (2019) 158–160.

[34] A. Judd, T. Backhaus, F. Goodsir, An effective set of principles for practical implementation of marine cumulative effects assessment, Environ. Sci. Policy 54 (2015) 254–262, https://doi.org/10.1016/j.envsci.2015.07.008.

[35] R. Joy, D. Tollit, J. Wood, A. McGillivray, Z. Li, K. Trounce, O. Robinson, Potential benefits of vessel slowdowns on endangered southern resident killer whales, Front. Mar. Sci. 6 (2019), https://doi.org/10.3389/fmsar.2019.00344 (26 June 2019).
[36] D.A. Keith, Assessing and managing risks to ecosystem biodiversity, Austral Ecol. 2015 (40) (2015) 337-346, https://doi.org/10.1111/aec.12249.

[37] A.M. Knights, G. Piet, R. Jongbloed, L.A. Robinson, An exposure-effect risk assessment methodology to evaluate the performance of management scenarios: case study examples from Europe's regional seas. Deliverable 9, EC FP7 Project (244273) "Options for Delivering Ecosystem Based Marine Management", University of Liverpool, 2013, p. 43 (ISBN: 978-0-906370-84-1).

[38] Lara, G., Bou-Cabo, M., Esteban, JA, Espinosa, V., Miralles, R., Design and application of a passive acoustic monitoring system in the Spanish implementation of the marine strategy framework directive, in: Proceedings of the 6th International Electronic Conference on Sensors and Applications, MDPI, 2019, pp. 1-7. (https://ecsa-6.sciforum.net/).

[39] J.C. Lilly, A.M. Miller, Vocal exchanges between dolphins: bottlenose dolphins "talk" to each other with whistles, clicks, and a variety of other noises, Science 134 (3493) (1961) 1873-1876, https://doi.org/10.1126/science.134.3493.1873.

[40] K. Lucke, U. Siebert, P.A. Lepper, M.-A. Blanchet, Temporary shift in masked hearing thresholds in a harbor porpoise (Phocoena phocoena) after exposure to seismic airgun stimuli, J. Acoust. Soc. Am. 125 (2009) 4060-4070, https://doi.org/10.1121/1.3117443.

[41] K.V. Mackenzie, Nine-term equation for sound speed in the oceans, J. Acoust. Soc. Am. 70 (1981) 807-808, https://doi.org/10.1121/1.386920.

[42] R.D. McCauley, F. Thomas, M.J.G. Parsons, C. Erbe, D. Cato, A.J. Duncan, Developing an underwater sound recorder: the long and short (time) of it, Acoust. Aust. 45 (2017) 301-311, https://doi.org/10.1007/s40857-017-0113-8.

[43] M. McKenna, D. Ross, S.M. Wiggins, J.A. Hildebrand, Underwater radiated noise from modern commercial ships, J. Acoust. Soc. Am. 131 (1) (2012) 92-103, https://doi.org/10.1121/1.3664100.

[44] L. Mee, P. Cooper, A. Kannen, A.J. Gilbert, T. O'Higgins, Sustaining Europe's seas as coupled social-ecological systems, Ecol. Soc. 20 (1) (2015) art1, https://doi.org/10.5751/ES-07143-200101.

[45] N.D. Merchant, M.J. Witt, P. Blondel, B.J. Godley, G.H. Smith, Assessing sound exposure from shipping in coastal waters using a single hydrophone and Automatic Identification System (AIS) data, Mar. Pollut. Bull. 64 (2012) 1320-1329, https://doi.org/10.1016/j.marpolbul.2012.05.004.

[46] R. Miralles Ricós, G. Lara Martinez, A. Carrion, J.A. Esteban, SAMARUC a programmable system for passive acoustic monitoring of cetaceans, Waves 5 (2013) 5-12. (http://hdl.handle.net/10251/52799).

[47] R. Miralles Ricós, G. Lara Martinez, J. Gosalvez, I. Bosch, A. Leon, Improved visualization of large temporal series for the evaluation of good environmental status, Appl. Acoust. 148 (2019) 55-61, https://doi.org/10.1016/j.apacoust.2018.12.009.

[48] MISTIC SEAS II, Deliverable 3. GRANT AGREEMENT No. 11.0661/2017/750679/SUB/ENV.C2: implementation of a coherent and coordinated sub-regional approach to monitoring and evaluation of marine biodiversity in Macaronesia for the second cycle of the Marine Strategy Framework Directive, 2019.

[49] OSPAR. Intermediate Assessment 2017: Impulsive Noise, 2017. (https://oap.ospar.org/en/ospar-assessments/intermediate-assessment-2017/pressures-human-activities/distribution-reported-impulsive-sounds-sea/).

[50] E. Pirotta, K.L. Brookes, I.M. Graham, P.M. Thompson, Variation in harbour porpoise activity in response to seismic survey noise, Biol. Lett. 10 (2014), 20131090, https://doi.org/10.1098/rsbl.2013.1090.

[51] A.N. Popper, M.C. Hastings, The effects of human-generated sound on fish, Integr. Zool. 4 (2009) 43-52, https://doi.org/10.1111/j.1749-4877.2008.00134.x.

[52] T. Potts, T. O'Higgins, R. Brennan, S. Cinnirella, U. Steiner Brandt, J.L. Suárez de Vivero, J. van Beusekom, T.A. Troost, L. Paltriguera, A. Gunduz-Hosgor, Detecting critical choke points for achieving good environmental status in European seas, Ecol. Soc. 20 (1) (2015) art29, https://doi.org/10.5751/ES-07280-200129.

[53] J. Raakjaer, J. vanLeeuwen, J. Jan vanTatenhove, M. Hadjimichael, Ecosystem-based marine management in European regional seas calls for nested governance structures and coordination—a policy brief, Mar. Policy 50 (B) (2014) 373-381, https://doi.org/10.1016/j.marpol.2014.03.007.

[54] W.J. Richardson, Marine mammal hearing, in: W.J. Richardson, Greene Jr., C. I. Malme, C.H. Thomson (Eds.), Marine Mammals and Noise, Academic Press, San Diego, CA, 1995, pp. 205-240.

[55] E. Rogan, P. Breen, M. Mackey, A. Cañadas, M. Scheidat, S. Geelhoed, M. Jessopp, Aerial Surveys of Cetaceans and Seabirds in Irish Waters: Occurrence, Distribution and Abundance in 2015-2017, Department of Communications, Climate Action & Environment and National Parks and Wildlife Service (NPWS), Department of Culture, Heritage and the Gaeltacht, Dublin, Ireland, 2018, p. 297.

[56] R. Sardà, T. O'Higgins, R. Cormier, A. Diedrich, J. Tintore, A proposed ecosystem-based management system for marine waters: linking the theory of environmental policy to the practice of environmental management, Ecol. Soc. 19 (4) (2014) art51, https://doi.org/10.5751/ES-07055-190451.

[57] R. Sardá, J.F. Valls, J. Pintó, E. Ariza, J.P. Lozoya, R.M. Fraguell, C. Martí, J. Rucabado, J.R. Ramis, J.A. Jimenez, Towards a new integrated beach management system: the ecosystem-based management system for beaches, Ocean Coast. Manag. 118 (2015) 167-177, https://doi.org/10.1016/j.ocecoaman.2015.07.020.

[58] R. Sardá, S.R. Moren, C. Dominguez-Carro, J.M. Gili, Ecosystem based mangement for marine protected areas: a network perspective, in: P.D. Goriup (Ed.), Management of Marine Protect Areas, John Wiley and Sons, 2017, pp. 145-162.

[59] C. Saavedra, B. Santos, P. Valcarce, L. Freitas, M. Silva, T. Pipa, J. Bécares, et al., MISTIC SEAS II Macaronesian Roof Report, 2018. (https://drive.google.com/file/d/18007F31ebB4QjiaFXxhtWb90ne1UEAs0/view/).

[60] M.P. Simmonds, S.J. Dolman, M. Jasy, E.C.M. Parsons, L. Weilgart, A.J. Wrigh, R. Leaper, Marine noise pollution-increasing recognition but need for more practical action, J. Ocean Technol. 9 (1) (2014) 71-90.

[61] H. Slabbekoorn, N. Bouton, I. van Opzeeland, A. Coers, C. ten Cate, A.N. Popper, A noisy spring: the impact of globally rising underwater sound levels on fish, Trends Ecol. Evol. 25 (7) (2010) 419-427, https://doi.org/10.1016/j.tree.2010.04.005.

[62] B.L. Southall, J.J. Finneran, C. Reichmuth, P.E. Nachtigall, D.R. Ketten, A. E. Bowles, W.T. Ellison, D.P. Nowacek, P.L. Tyack, Marine mammal noise exposure criteria: updated scientific recommendations for residual hearing effects, Aquat. Mamm. 45 (2) (2019) 125-232, https://doi.org/10.1578/AM.45.2.2019.125.

[63] P.M. Thompson, K.L. Brookes, I.M. Graham, T.R. Barton, K. Needham, G. Bradbury, N.D. Merchant, Short-term disturbance by a commercial two-dimensional seismic survey does not lead to long-term displacement of harbour porpoises, Proc. Biol. Sci. 280 (2013), 20132001, https://doi.org/10.1098/rspb.2013.2001.

[64] J. Tougaard, J. Carstensen, J. Teilmann, H. Skov, P. Rasmussen, Pile driving zone of responsiveness extends beyond 20 km for harbour porpoises (Phocoena phocoena (L.)), J. Acoust. Soc. Am. 126 (2009) 11-14, https://doi.org/10.1121/1.3132523.

[65] P. Tyack, E.H. Miller, Vocal anatomy, acoustic communication and echolocation, in: A.R. Hoelzel (Ed.), Marine Mammal Biology: An Evolutionary Approach, Blackwell Science, Oxford, 2002, pp. 142-184.

[66] United Nations, Nineteenth Meeting of the United Nations Open-Ended Informal Consultative Process on Oceans and the Law of the Sea: Anthropogenic Underwater Noise, 18-22 June 2018, New York, 2018.

[67] United Nations Economic Commission for Europe, Convention on Access to Information, public participation in decision-making and access to justice in environmental matters, 25 June 1998 (Aarhus Convention), UNECE, Geneva, 1998.

[68] J. van Tatenhove, J. Raakjaer, J. van Leeuwen, L. van Hoof, Regional cooperation for European seas: governance models in support of the implementation of the MSFD, Mar. Policy 50 (2014) 364-372, https://doi.org/10.1016/j.marpol.2014.02.020.

[69] E. Verling, M.B. Cabo, G. Carreira, M. Garagouni, G. Lara, V. Lopes, R. Miralles, J. Macedo, T. O'Higgins, J. Otero, M. Silva, A.P. Simão. Applying the RAGES Risk-Based Approach to MSFD Descriptor 11, Underwater Noise. RAGES Deliverable 4.1-4.3. Risk Based Approaches to Good Environmental Status Project, 2021, p. 31. D. Wang, B. Würsig, W.E. Evans, Whistles of bottlenose dolphins: comparisons among populations, Aquat. Mammal. 21 (1995) 65-77.

[70] G.M. Wenz, Acoustic ambient noise in the ocean: spectra and sources, J. Acoust. Soc. Am. 34 (1962) 1936-1956, https://doi.org/10.1121/1.1909155.

[71] R. Williams, A.J. Wright, E. Ashe, L.K. Blight, R. Bruintjes, R. Canessa, C.W. Clark, S. Cullis-Suzuki, D.T. Dakin, C. Erbe, P.S. Hammond, N.D. Merchant, P.D. O'Hara, J. Purser, A.N. Radford, S.D. Simpson, L. Thomas, M.A. Wale, Impacts of anthropogenic noise on marine life: publication patterns, new discoveries, and future directions in research and management, Ocean Coast. Manag. 115 (2015) 17-24, https://doi.org/10.1016/j.ocecoaman.2015.05.021.