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Cook Inlet Beluga Whale PCoD Expert Elicitation Workshop Report Prepared for NOAA Fisheries [September, 2016]

SMRU Consulting North America

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Cook Inlet Beluga Whale PCoD Expert Elicitation Workshop Report

Prepared by SMRU Consulting North America

Authors:

Dominic Tollit, PhD

John Harwood, PhD

Cormac Booth, PhD

Len Thomas, PhD

Leslie New, PhD

Jason Wood, PhD

29th September 2016

Cook Inlet Beluga Whale PCoD Expert Elicitation Workshop Report (2016). Prepared by SMRU Consulting North America. Tollit, D.J., Harwood, J., Booth, C.G., Thomas, L., New, L., and Wood, J.D., 29 September 2016, SMRUC-NA-NOAA0915, 54p.

For its part, the Buyer acknowledges that Reports supplied by the Seller as part of the Services may be misleading if not read in their entirety, and can misrepresent the position if presented in selectively edited form. Accordingly, the Buyer undertakes that it will make use of Reports only in unedited form, and will use reasonable endeavours to procure that its client under the Main Contract does likewise. As a minimum, a full copy of our Report must be appended to the broader Report to the client.

Cook Inlet beluga whale PCoD Workshop

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Table of Contents

1. Introduction ............................................................................................................................. 5 1.1 Summary of the Interim PCoD Framework .................................................................................................. 8

1.1.1 Outputs of interim PCoD ....................................................................................................................................... 10

2. CIBW Expert Elicitation Workshop ............................................................................................. 11 2.1 Project Goals ................................................................................................................................................. 11 2.2 Background Presentations ....................................................................................................................... 12 2.3 Potential Noise Effect Mechanisms for CI beluga whales ............................................................... 12 2.4 CIBW Beluga elicitation .................................................................................................................................... 14

2.4.1 Calibration question .................................................................................................................................................... 15 2.4.2 Mechanism 1: Near-term pregnant females fail to gain enough energy during spring and either terminate pregnancy or abandon calf at birth............................................................................................................. 17 2.4.3 Mechanism 2: Lactating females fail to gain sufficient energy by the end of summer to maintain themselves and their calves during the subsequent winter .................................................................................. 20 2.4.4 Linking body condition and vital rates ................................................................................................................ 23

2.5 Incorporating other noise effect mechanisms into interim PCOD or PVA models ................... 25 2.6 Research that could improve understanding of the effects of noise on CI beluga whales ... 26 2.7 Potential improvements to the expert elicitation process .......................................................... 27

3. Incorporating the expert elicitation workshop results into an interim PCoD framework ......... 27

4. An illustrative application of the CI beluga whale interim PCoD framework ............................ 29

5. Appendix 1 - Workshop Participants, Roles and Declarations of Interest/expertise ................ 31

6. Appendix 2 - Workshop Agenda ............................................................................................. 34

7. Appendix 3 - Elicitation process ............................................................................................ 36

8. Appendix 4 - Briefing documents provided to experts before workshop ................................. 39

9. Literature Cited in Report ...................................................................................................... 51

10. Glossary of Terms ................................................................................................................ 54


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List of Figures Figure 1 - Fig. 13 from NMFS (2015) showing abundance estimates for Cook Inlet beluga whales between

1994 and 2014. Vertical bars indicate the 95% Confidence Interval for each estimate. The 10 year trend from 2004 to 2014 (indicated by the red line) was -0.4% per year (SE = 1.3%). ....................... 6

Figure 2 - The Population Consequences of Acoustic Disturbance (PCAD) model developed by the National Research Council’s panel on the biologically significant effects of noise. After Fig. 3.1 in NRC (2005). The number of + signs indicates the panel’s evaluation of the relative level of scientific knowledge about the links between boxes, 0 indicates no knowledge. These links were described by the panel as “transfer functions”. ..................................................................................................... 8

Figure 3a - A conceptual model of the population consequences of disturbance (PCoD) developed by the ONR Working Group on PCAD (modified from Fig.4 of New et al. 2014). The term “health” is used to describe all aspects of the internal state of an individual that might affect its fitness. These could include, for example, the extent of its lipid reserves or its resistance to disease. “Vital rates” refers to all the components of individual fitness (probability of survival and producing offspring, growth rate, and offspring survival). The arrows represent functions that describe the mathematical relationships between the boxes that they link. ................................................................................... 9

Figure 4 - The hypothetical relationship used in the expert elicitation relating the number of days of disturbance experienced by a female CIBW and its effect on her energy reserves. ........................ 14

Figure 5 - Statistical distributions for the number of days of disturbance (x-axis) that a pregnant female beaked whale can sustain over a 1 year period without it affecting her blubber reserves at parturition predicted by individual experts. ........................................................................................ 15

Figure 6 - The consensus statistical distribution agreed by the experts for the number of days of disturbance (x-axis) that a pregnant female beaked whale can sustain over a 1 year period without it affecting her blubber reserves at parturition. The red line is the average of the distributions proposed by each of the experts, and the black line is the distribution agreed by consensus. The black vertical lines represent the upper and lower quartiles of the distribution. ............................. 16

Figure 7 - Minimum number of days of disturbance that a pregnant female beaked whale can sustain over a 1 year period without it affecting her blubber reserves at parturition. Based on simulated exposure histories for 1000 females using the energetics model developed by New et al. (2013).16

Figure 8 - Statistical distributions chosen by each expert in answer to the question, “How many days of disturbance (x-axis) can a pregnant female CIBW tolerate in the period April, May and June before there will be a reduction in her energy reserves when she gives birth?” The group average (linear pool) distribution is shown by the thick dashed red line. ................................................................... 17

Figure 9 - Group consensus distribution (black line) and group average distribution (red line) derived from answers to the question, “How many days of disturbance (x-axis) can a pregnant female CIBW tolerate in the period April, May and June before there will be a reduction in her energy reserves when she gives birth?” ......................................................................................................................... 18

Figure 10 - Statistical distributions chosen by each expert in answer to the question, “How many days of disturbance (x-axis) in the period April, May and June would be required to reduce the energy reserves of a pregnant CIBW to such a level that she is certain to terminate the pregnancy or abandon the calf soon after birth?” The group average (linear pool) distribution is shown by the thick dashed red line. ............................................................................................................................ 19

Figure 11 - Group consensus distribution (black line) and group average distribution (red line) in response to the question, “How many days of disturbance (x-axis) in the period April, May and June would be


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required to reduce the energy reserves of a pregnant CIBW to such a level that she is certain to terminate the pregnancy or abandon the calf soon after birth?” ..................................................... 19

Figure 12 - Statistical distributions chosen by each expert in answer to the question, “How many days of disturbance (x-axis) can a female CIBW who is lactating tolerate in the period April-September before her energy reserves at the end of September will be insufficient to maintain her and her calf through the winter?” The group average (linear pool) distribution is shown by the thick dashed red line. ........................................................................................................................................................ 20

Figure 13 - Group consensus distribution (black line) and group average distribution (red line) agreed in response to the question, “How many days of disturbance (x-axis) can a female CIBW who is lactating tolerate in the period April-September before her energy reserves at the end of September will be insufficient to maintain her and her calf through the winter?” ............................................. 21

Figure 14 - Statistical distributions chosen by each expert in answer to the question, “How many days of disturbance (x-axis) in the period April-September would be required to reduce the energy reserves of a lactating CIBW to a level that she is certain to abandon her calf?” The group average distribution is shown by the thick dashed red line. ............................................................................ 22

Figure 15 - Group consensus distribution (black line) and group average distribution (red line) in response to the question, “How many days of disturbance (x-axis) in the period April-September would be required to reduce the energy reserves of a lactating CIBW to a level that she is certain to abandon her calf?” ............................................................................................................................................... 22

Figure 16 - The predictions of 250 “virtual” experts of the relationship between the number of days of disturbance experienced by a pregnant female CIBW in April, May and June and the probability that she will give birth to her calf. The density of the lines provides an indication of how frequently particular combinations of parameter values are likely to be chosen. .............................................. 24

Figure 17 - The predictions of 250 “virtual” experts of the relationship between the number of days of disturbance experienced between April and September by a pregnant or lactating female CIBW and the survival rate of her calf. The density of the lines provides an indication of how frequently particular combinations of parameter values are likely to be chosen. The horizontal line represents situations where the “virtual” expert predicts that disturbance will have no effect on the baseline calf survival rate of 0.9. ......................................................................................................................... 24

List of Tables Table 1. Details of presentations made at the April 2016 workshop ......................................................... 12

Table 2. Proposed noise effect mechanisms and the vital rate they are likely to affect. .......................... 13

Table 3. Ways in which the noise effect mechanisms not considered as part of the expert elicitation could be incorporated into population models ............................................................................................. 25


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1. Introduction Policy makers and managers are increasingly concerned about the effects of disturbance on wildlife populations (Sutherland et al. 2006). However, forecasting population-level consequences of changes in individual behaviour is difficult (Sutherland et al. 2013). Working groups established by the US National Academy of Sciences and the US Office of Naval Research (ONR) have developed conceptual models of the population consequences of disturbance (PCoD) for marine mammals (NRC 2005; New et al. 2014). These models build on the concept that many species appear to perceive human disturbance as a predation threat (Frid & Dill 2002, Beale & Monaghan 2004). This approach reflects Sutherland’s (1996) definition of disturbance as “adverse behavioural changes in animals as a result of predators or humans”. New et al. (2014) considered behavioural and physiological responses to disturbance as adverse if they had a negative effect on individual health (defined as “all aspects affecting individual fitness”) (see Glossary of terms). Using data from a long-term study of southern elephant seals (Mirounga leonina) they showed how reduced foraging time resulting from disturbance could affect breeding females’ health (measured by lipid mass), reducing offspring survival and population growth rate. Although there is an extensive literature documenting the effect of human disturbance on wildlife behaviour (e.g. Blumstein et al. 2005, Barber et al. 2010), the fitness consequences of behavioural changes have been demonstrated for only a few species (e.g., New et al. 2014). For most species, there is little or no empirical evidence to quantify the relationship between behavioural or physiological change and fitness. To fill this knowledge gap, we have developed an interim version of the PCoD framework (termed “interim PCoD” or “iPCoD”) that uses expert elicitation to provide values for the parameters of a set of hypothetical relationships between disturbance and fitness. The approach is considered to be “interim”, because the values provided by experts should be replaced with empirically-derived values as soon as they become available. In order to issue an incidental harassment authorization to U.S. citizens and U.S.-based organizations under the Marine Mammal Protection Act (MMPA), the Office of Protected Resources must ensure that “the specified activity …cannot be reasonably expected to, and is not reasonably likely to, adversely affect the species or stock through effects on annual rates of recruitment or survival”. Authorization of incidental take under the MMPA only occurs if the National Marine Fisheries Service (NMFS) find the take would have no more than a "negligible impact" on those marine mammal species or stocks, and not have an "unmitigable adverse impact" on the availability of the species or stock for "subsistence" uses. Under the 1973 Endangered Species Act, for any listed species, a Section 7 consultation is required on any Federal actions that may affect a listed species to minimize the effects of the action. These interagency consultations are designed to assist Federal agencies in fulfilling their duty to ensure Federal actions do not jeopardize the continued existence of a species or destroy or adversely modify critical habitat. Should an action be determined by NMFS to jeopardize a species or adversely modify critical habitat, NMFS will suggest Reasonable and Prudent Alternatives (RPAs). This project focused specifically on the Cook Inlet beluga whales (Delphinapterus leucas) (CIBW) “distinct population segment” and aims to address three elements of the strategy of the NMFS recovery plan:

1) early stage development of a new assessment tool addressing whether noise is likely to be limiting recovery of the CIBW population;

2) potential improvements to understanding the effects of recovery-limiting threats to CIBW; and


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3) potential improvements to management of the recovery-limiting threats to reduce the effects of those threats on CIBW.

The CIBW population declined from around 1,300 whales in 1979 to an estimated 367 in 1999 (Hobbs et al. 2000) (Figure 1). Alaskan Native subsistence harvest between 1993 and 1998 ranged from 21 in 1994 to 123 in 1996 with the most reliable data coming from 1995 – 1997 when an average of 87 whales were taken per year over that period (Angliss and Lodge, 2002). The subsistence take was sufficient to account for most of the observed decline in the beluga population over this period. Alaskan Natives imposed a voluntary moratorium in 1999, and in 2000 NMFS declared the population depleted under the Marine Mammal Protection Act (65 FR 34590) and later listed as endangered under the ESA (73 FR 62919, October 22, 2008). Since 1999 the total subsistence harvest has been five whales, with none taken after 2005 (NMFS, 2015). Nonetheless, the population has continued to decline. The most recent estimate is 340 in 2014 (Shelden et al. 2015). In May 2015 NMFS deemed CIBW a “Species in the Spotlight”, one that is highly at risk of extinction. A population viability analysis (PVA) (Hobbs et al. 2008) showed high probabilities of extinction in 100 years and that it was likely that the CIBW population will continue to decline over the next 300 years, unless factors determining this population’s growth and survival are altered in its favor. In addition to the observed population decline, the core summer distribution of the CIBW contracted from over 7,000 km2 in 1978-79 to 2,800 km2 in 1998-2008 (Rugh et al. 2010). As a result, most of the population is concentrated in upper Cook Inlet, close to the port of Anchorage, during the summer months, where it is most likely to be exposed to disturbance from human activities (NMFS, 2015).

Figure 1 - Fig. 13 from NMFS (2015) showing abundance estimates for Cook Inlet beluga whales between 1994 and 2014. Vertical bars indicate the 95% Confidence Interval for each estimate. The 10 year trend from 2004 to 2014 (indicated by the red line) was -0.4% per year (SE = 1.3%).


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The National Marine Fisheries Service (NMFS 2015) identified 10 key threats (excluding climate change) to CIBW recovery, with anthropogenic noise ranked as one of three threats of “high relative concern”.

Threats of High Relative Concern o Catastrophic Events (e.g., natural disasters; spills; mass strandings) o Cumulative Effects of Multiple Stressors o Noise

Threats of Medium Relative Concern o Disease Agents (e.g., pathogens, parasites, and harmful algal blooms) o Habitat Loss or Degradation o Reduction in Prey o Unauthorized Take

Threats of Low Relative Concern o Subsistence Hunting o Pollution o Predation

The effect of anthropogenic noise, particularly the combined effect of different sound sources occurring simultaneously or consecutively, has the potential to affect beluga acoustic perception, communication, echolocation, and behavior (such as foraging and movement patterns). In the long term, anthropogenic noise may induce chronic effects altering the health of individual CIBW, which in turn may decrease survival and reproduction, with consequences at the population level. Although the direct and indirect effects of these sounds on CIBW are currently unknown, NMFS (2015) concluded there was a high potential for negative impacts based on evidence from other odontocete species (including other beluga populations). Anthropogenic noise also has the potential to indirectly affect the survival and reproduction success of CIBW by having negative effects on their prey. Depending on the source, a noise can be localized or occur range-wide, with a variable frequency of occurrence depending on the source of the noise. While noise may result in compromised communication and hearing of the CIBW and may contribute to habitat degradation, the magnitude of the impact of noise on CIBW is unknown, but potentially high. There is a high probability that anthropogenic noise in Cook Inlet will continue and increase in the future, and given that the natural noise is already limiting, NMFS (2015) considered the threat to CIBW recovery due to anthropogenic noise to be of high concern. Understanding how noise-related stressors might affect vital rates (survival, birth rate and growth) for CIBW and the best way to model those effects, are essential components of an iPCoD approach (King et al. 2015). NMFS (2015, section IX.D - CI Beluga Hearing, Vocalization, and Noise Supplement, reproduced in Appendix 5) suggests that the main direct effects of noise on CIBW are likely to be through masking of vocalizations used for communication and prey location, and habitat degradation. Changes in the vocal behavior that may compensate for the effects of masking (i.e., the Lombard effect) have been recorded for belugas in the St Lawrence Estuary (Sheifele et al. 2005), but such changes are likely to incur energetic costs (Holt et al. 2015). Belugas in the Arctic have also been observed to temporarily vacate areas in response to pulsed sound from seismic surveys and ice-breaker noise (see Appendix A of NMFS 2015). In addition, there may be synergistic effects between noise and certain chemical pollutants, although NMFS (2015) notes that “these synergistic effects have not yet been described in marine mammals”.


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CIBW are considered a well-studied endangered stock, exposed to a variety of anthropogenic and environmental threats (Norman 2011, NMFS 2015), including increasing levels of acoustic disturbance. However, for beluga there is a lack of appropriate datasets linking exposure to disturbance to behavioral change, and behavioral change to health. This currently limits our ability to construct a “full” PCoD model (i.e., one parameterized by empirical data). However, regulators and their scientific advisors still need to make decisions about the potential effects of disturbance in such situations. Until such data are available and linkages are better understood, the interim iPCoD approach can provide regulators with a tool to help understand whether chronic and acute anthropogenic noise from various sources and projects are likely to be limiting recovery of the CIBW population.

1.1 Summary of the Interim PCoD Framework In 2005 a panel convened by the National Research Council of the United States National Academy of Sciences (NRC) published a report on “Marine Mammal Populations and Ocean Noise: Determining When Noise Causes Biologically Significant Effects”. The panel developed what it called “a conceptual model” that outlined how marine mammal might be affected by anthropogenic noise and how population level effects could be inferred on the basis of observed behavioral changes. They called this model Population Consequences of Acoustic Disturbance (PCAD; Figure 2).

Figure 2 - The Population Consequences of Acoustic Disturbance (PCAD) model developed by the National Research Council’s panel on the biologically significant effects of noise. After Fig. 3.1 in NRC (2005). The number of + signs indicates the panel’s evaluation of the relative level of scientific knowledge about the links between boxes, 0 indicates no knowledge. These links were described by the panel as “transfer functions”.

In 2009 the US Office of Naval Research (ONR) set up a working group to transform this framework into a formal mathematical structure and determine how that structure could be parameterized using data from a number of case studies. The ONR working group extended the PCAD framework so that it could be used to consider other forms of disturbance and to address the impact of disturbance on physiology


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as well as behavior. The current version of that framework is now known as PCoD (Population Consequences of Disturbance); it is shown in Figure 3a and described in more detail in New et al. (2014). The PCoD framework illustrates how disturbance may impact both the behavior and physiology of an individual, and how changes in these variables may affect that individual’s vital rates (its probability of survival and of producing an offspring) either directly (an acute effect) or indirectly via its health (a chronic effect). For example, behavioral changes in response to disturbance may have an acute effect on survival if they result in a calf being separated from its mother, or if they affect an individual’s ability to recover from the effects of deep dives, increasing its risk of decompression stress (Hooker et al. 2012). They may have chronic effects on reproduction if disturbed animals spend less time feeding, and therefore acquire less energy, or they spend less time in activities like resting, that conserve energy. Identifying and quantifying species-specific “key pathways of effect” for particular sources of noise disturbance are a key requirement of any PCoD model.

Figure 3a - A conceptual model of the population consequences of disturbance (PCoD) developed by the ONR Working Group on PCAD (modified from Fig.4 of New et al. 2014). The term “health” is used to describe all aspects of the internal state of an individual that might affect its fitness. These could include, for example, the extent of its lipid reserves or its resistance to disease. “Vital rates” refers to all the components of individual fitness (probability of survival and producing offspring, growth rate, and offspring survival). The arrows represent functions that describe the mathematical relationships between the boxes that they link.

Figure 3b - The simplified version of the ONR working group framework used in the interim PCoD protocol. The transfer functions that determine the chronic effects of physiological and behavioral change on vital rates are represented with dotted lines to indicate that their mathematical form is determined using the results of an expert elicitation process rather than empirical evidence.


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Considerable progress has been made recently in developing and parameterizing PCoD frameworks (e.g., Nabe-Nielsen et al. 2013, New et al. 2014). The specific approach utilized is highly dependent on the quality of data available for the candidate population and the anthropogenic stressor(s) being assessed. One potential approach when empirical data are limited is to use expert elicitation (Runge et al. 2011, Martin et al. 2012) to parameterize some of the transfer functions of the general PCoD framework (Fig. 2). Expert elicitation is a formal process in which a number of experts on a particular topic are asked to predict what may happen in a particular situation. It is designed to mitigate the well-documented problems that arise when expert judgments are canvassed naïvely. These problems include anchoring, availability bias, confirmation bias and overconfidence. The experts’ predictions are combined into calibrated, quantitative statements, with associated uncertainty that can be incorporated into mathematical models (Martin et al. 2012). Harwood et al. (2014) and King et al. 2015) used this approach to assess the potential effects of sounds associated with the construction of offshore wind farms on populations of five marine mammal species in the North Sea. This approach has been termed “Interim PCoD” and has been utilized by UK offshore wind farm developers and their consultants, by UK regulators and Statutory Nature Conservation Advisors. It has also been used in a cumulative impact assessment for offshore wind development in the Netherlands, which was funded by Rijkswaterstaat (Heinis et al. 2015). In addition, SMRU Consulting and CREEM have recently investigated the potential of the interim PCoD approach for assessing the population-level consequences of disturbance from Navy sonar on Blainville’s beaked whales (Mesoplodon densirostris) and sperm whales (Physeter macrocephalus) (Booth et al. 2016; Harwood & Booth 2016). Interim PCoD model outputs are based upon repeated simulations of identical pairs of populations that differ only in whether or not they are exposed to disturbances. The approach takes account of uncertainty in the following inputs and assumptions:

The number of animals disturbed per day as a result of different kinds of activity

The size and status of the population that is likely to be affected by these activities

The duration of the disturbance response

The vulnerability of different components of the population to the effects of these activities (for example, some individuals may spend almost all their time in a particular area, whereas others may only visit it on one or two occasions per year)

Variations among experts in the predicted effect of disturbance on vital rates

Environmental stochasticity (variations in vital rates among years)

Demographic stochasticity (the fact that the number of births and deaths in a population will vary among years because of chance events, even if vital rates are constant)

1.1.1 Outputs of interim PCoD The population trajectories forecast by interim PCoD vary substantially among simulations of exactly the same disturbance scenario because of the sources of uncertainty that are incorporated into the model. This variability, which is an attempt to capture as much quantifiable uncertainty as possible, can make it difficult to interpret the outputs from the framework. Therefore, a number of summary outputs, which can be tuned to the requirements of the relevant regulators, are provided. King et al. (2015) provided estimates of the risk that the North Sea harbor porpoise population would decline by more than 1% per annum over a 12 year period, because this is a potential indicator of “unfavorable conservation status” in the terms of European legislation. Harwood & Booth (2016) suggested that ΔR (the difference between


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the growth rates of two otherwise identical populations, only one of which is subject to disturbance) might be a useful output metric for beaked whale populations in US waters. For each disturbance scenario, they determined the statistical distribution of ΔR based on hundreds of simulations, and then calculated the probability that ΔR would exceed ΔRPBR (the short term change in net productivity rate of the population if the number of animals specified by a PBR calculation was removed from the population) at different time intervals. In the case of CIBW, a comprehensive population dynamics model that takes account of many of the same sources of uncertainty as interim PCoD is already available (Hobbs et al. 2008, Hobbs et al. 2016). This model can provide detail predictions of future population trajectories under a wide range of different scenarios. Rather than duplicate the predictions of this model using interim PCoD, we have modified the model so that it can provide forecasts of the likely effects of different development scenarios on the vital rates of the CIBW population, and the uncertainty associated with these forecasts, in a form that can easily be incorporated into the model of Hobbs et al. (2016).

2. CIBW Expert Elicitation Workshop

2.1 Project Goals

The main objective of this project was to conduct a workshop to investigate how specific noise-related stressors might affect CIBW vital rates, and the best way to model those effects, in the context of other stressors, using the interim PCoD framework. The aims of the workshop were to:

1. Provide summary presentations of ONR-funded PCoD studies and the interim PCoD approach, including an overview of a marine mammal expert elicitation process.

2. Provide summary presentations on the state of scientific knowledge of CIBW biology in relation to key stressors.

3. Identify potential noise-related threats (e.g., geophysical surveys, construction projects, exploratory drilling, and associated vessel traffic and chronic noise exposure).

4. Discuss how these threats might affect CIBW vital rates (termed noise effect mechanisms) and the best ways to model those effects.

5. Conduct an expert elicitation on these noise effect mechanisms for CIBW. 6. Identify key research gaps that would improve understanding of potential noise effects and other

interacting stressors.

Information from the workshop was used to develop a set of mathematical functions relating CIBW survival and fertility rates to levels of noise disturbance. These functions were then incorporated into a revised version of the interim PCoD software. The workshop was held on 26-28 April 2016 at the NOAA laboratory in Seattle, WA. A list of workshop participants and a copy of the Agenda can be found in Appendices 1 and 2.


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2.2 Background Presentations A series of presentations (Table 1) were made at the start of the workshop in order to supplement the information in the briefing documents (Appendix 3) that had been circulated prior to the workshop.

Table 1. Details of presentations made at the April 2016 workshop

Presenter Information presented

Dom Tollit Project overview and goals

John Harwood PCoD and interim PCoD model background

Rod Hobbs CIB population demographics, spatial use and PVA

Manuel Castellote Acoustic environment of Cook Inlet, PAM studies and noise effect studies

Greg Balogh NOAA regulatory perspective

Robert Small ADFG regulatory perspective

Tamara McGuire Spatial distribution of anthropogenic stressors

John Harwood SHELF process and probability explanation

John Harwood/Leslie New Bioenergetic models for beaked whales

John Harwood Energetic demands of pregnant and lactating CIB

2.3 Potential Noise Effect Mechanisms for CI beluga whales Initial workshop discussions focussed on developing a suite of potential noise effect mechanisms. The following provides a summary of the main topic areas and some of the key elements in discussions. There was considerable discussion on habitat use, principally that CIBW appear to stay in Cook Inlet throughout their lives, but they exhibit seasonal shifts in distribution and habitat use within the Inlet. These seasonal shifts appear to be related to corresponding changes in the physical environment (e.g., ice cover and currents) and food sources, specifically the timing of fish runs. Although belugas may be found anywhere in Cook Inlet at any time of year, most CIBW are believed to spend the ice-free months in upper Cook Inlet, often in discrete high-use areas where anadromous fish (notably eulachon and salmon species) are plentiful. They tend to head south to the deeper waters of middle Cook Inlet in winter, but there is only limited information on how beluga whales use the waters of Cook Inlet at this time of year. Various hypotheses for the observed contraction in range in recent years were also discussed. There was considerable discussion of different acute and chronic noise effects, such as masking and stress, as well as the temporal and spatial variation in anthropogenic activities occurring in Cook Inlet. Experts expressed concern on a number of elements; that chronic effects might occur at received noise levels below the current NMFS behavioural response thresholds, about the current lack of knowledge on the potential fitness consequences of masking, and regarding the spatial displacement of beluga whales by noise. There was also discussion on unusual mortality events, pollutants and contaminants. It was noted that the absence of toxins in the ranking of threats in NMFS (2015) was due to lack of data, not a lack of concern regarding the stressor. Table 2 provides a list of the main noise effect mechanisms discussed at the workshop. The general view was that disturbance was likely to be most important during the summer months. Cornick et al. (2016)


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found that belugas sampled in the Eastern Chukchi Sea and Bristol Bay, Alaska lost 30% of their blubber stores between fall and spring. This suggests that Alaskan belugas cannot obtain sufficient food during the winter months to meet their daily energetic requirements. As a result, lactating and pregnant females must build up substantial blubber reserves during the summer if they and their offspring are to survive through the winter months. Any disturbance that reduces their ability to accumulate these reserves is likely to affect their probability of giving birth or the survival of their calf. The experts thought that the most likely effect of anthropogenic noise would be to prevent or deter animals from entering a river mouth at the appropriate tidal state during a fish run (identified as key foraging locations for high energy prey like salmonids and eulachon). As a result, experts estimated animals would forgo 50-100% of their energy intake on that day. Mechanisms 1, 2 and 8 in Table 2 are based on the fact that the last months of pregnancy and the entire lactation period are periods of very high energy demand for adult females. For example, a captive female beluga more than doubled her food intake during the last 2 months of her pregnancy (Kastelein et al. 1994). If the energetics model developed by New et al. (2013) for beaked whales also applies to belugas, a lactating female requires 40-50% more energy per day than a non-lactating female. Table 2. Proposed noise effect mechanisms and the vital rate they are likely to affect.

Noise effect mechanism Vital rate affected

1. Near-term pregnant females fail to gain enough energy during spring and either terminate pregnancy or abandon calf at birth

Birth rate/calf survival

2. Lactating females fail to gain sufficient energy by the end of summer to maintain themselves and their calves during the subsequent winter*

Calf survival or birth rate (because of increased duration of lactation)

3. High levels of stress caused by chronic exposure to noise and interaction with masking and other disturbance events*

All vital rates

4. Non-pregnant females fail to gain enough energy to become pregnant in the following spring

Birth rate

5. Mother-calf pairs are separated by a disturbance event and cannot restore connection, because of e.g., masking

Calf survival

6. Animals fail to detect approaching killer whale groups as a result of PTS from historical noise exposure, TTS and/or distraction and masking from current noise exposure

All survival rates

7. Strandings as a response to acute sound exposure or reduced ability to navigate because of PTS or masking of echolocation signals

All survival rates

8. Reduced energy intake during winter because of masking and displacement due to noise*

All survival rates, but particularly calf survival

*exacerbated by energetic costs of responding to Lombard effect

Each of the mechanisms was discussed in terms the scale of its effects and its suitability for expert elicitation. It was noted that experts often find it difficult to predict very low probability events with any accuracy (see, e.g., Booth et al. 2016). Mechanisms 5, 6 and 7 were all considered to fall into this category and were therefore considered to be unsuitable for expert elicitation at the workshop. Chronic effects, such as those resulting from mechanism 3, are difficult to incorporate into Interim PCOD models, and there is limited information on prey availability for CIBW during the winter. On this basis, it was agreed


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to focus the expert elicitation initially on mechanisms 1 and 2. Ways in which the other mechanisms could be incorporated into population models for CIBW are discussed in section 2.7.

2.4 CIBW Beluga elicitation Expert elicitation was used to obtain values for the parameters relating to the noise effect mechanisms outlined in section 2.3, and the uncertainty associated with these values. The elicitation was conducted using the Sheffield Elicitation Framework (SHELF V2.0), which is based on the elicitation practice recommended in O’Hagan et al. (2006), and described in Appendix 3. Experts were asked to provide a plausible range for each parameter, such that it is extremely unlikely (but not necessarily impossible) that the true value of the parameter (X) lies outside this range. They were also asked to provide a median value. This is a value such that “X lies below the median” and “X lies above the median‟ are equally likely propositions. Finally, they were asked to estimate the upper and lower quartiles for X. This involved dividing the range from the lowest plausible value to the median into two equally likely intervals, to provide an estimate of the lower quartile. The same process is used to estimate the upper quartile, which divides the range from the median to the highest plausible value into two equally likely intervals. Each phase of the process involved an individual stage, where each group member identified the parameter value(s) of interest independently, and a group stage, where the distribution of values chosen were discussed and a consensus arrived at - see Appendix 3 for details. A total of 17 experts took part in the elicitation process (see Appendix 1). For each of the noise effect mechanisms chosen for expert elicitation we asked the experts to provide a set of parameter values that determined the form of a relationship between the number of days of disturbance a female CIBW experiences in a particular period and the effect of that disturbance on her energy reserves, as shown in Figure 4. A day of disturbance was defined as any day on which an animal loses the ability to forage for at least one tidal cycle (i.e., it forgoes 50-100% of its energy intake on that day).

Figure 4 - The hypothetical relationship used in the expert elicitation relating the number of days of disturbance experienced by a female CIBW and its effect on her energy reserves.


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2.4.1 Calibration question As part of their training for the process of elicitation, experts were asked a calibration question to provide them with feedback on the consequences of choosing particular combinations of parameter values. The calibration question was based on outputs from the energetics model for beaked whales developed by New et al. (2013). Experts were asked to provide their opinion on “how many days of disturbance (spread across a one year period) associated with Navy activity can a pregnant beaked whale sustain without it affecting her blubber reserves at parturition?”. The experts firstly agreed that a plausible range for this parameter was 12-100 days, and then independently selected a mean, lower quartile and upper quartile within these bounds. Figure 5 shows the statistical distributions predicted by the individual experts, and the mean distribution (labelled “linear pool”).

Figure 5 - Statistical distributions for the number of days of disturbance (x-axis) that a pregnant female beaked whale can sustain over a 1 year period without it affecting her blubber reserves at parturition predicted by individual experts.

The individual distributions are highly variable. In a number of cases one or both of the quartiles was close to the maximum or minimum plausible value. This resulted in an infeasible distribution with peaks at the upper and lower bounds. As a result, the average distribution (red line in Fig. 6) suggests that values close to the limits are more likely than values closer to the middle of the range. The experts agreed on a consensus distribution that matched the quartiles of the average distribution (shown as vertical lines in Fig. 6) but which had a more conventional shape, best described by the log-normal distribution shown by the black line in Fig. 6.


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Figure 6 - The consensus statistical distribution agreed by the experts for the number of days of disturbance (x-axis) that a pregnant female beaked whale can sustain over a 1 year period without it affecting her blubber reserves at parturition. The red line is the average of the distributions proposed by each of the experts, and the black line is the distribution agreed by consensus. The black vertical lines represent the upper and lower quartiles of the distribution.

Figure 7 shows the equivalent distribution predicted by the energetics model on which the experts were asked to base their predictions. The median number of days of disturbance that a female can sustain without it affecting her energy reserves at parturition predicted by the energetics model was 43, identical to the median value provided by the experts. The lower and upper quartiles from the model were 36 and 48 days, respectively, whereas the equivalent inter-quartile interval from the experts’ concensus distribution was 34-62 days. This suggests that, although there was wide variation among the predicted distributions provided by individual experts, in concensus they were able to choose a statistical distribution (Fig. 6) that was close to the outputs of a bioenergetic model of the effects of disturbance (Fig. 7).

Figure 7 - Minimum number of days of disturbance that a pregnant female beaked whale can sustain over a 1 year period without it affecting her blubber reserves at parturition. Based on simulated exposure histories for 1000 females using the energetics model developed by New et al. (2013).


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2.4.2 Mechanism 1: Near-term pregnant females fail to gain enough energy during spring and either terminate pregnancy or abandon calf at birth

It should be recognized that all of these estimates are based either on strong assumptions or on the opinions of the experts we consulted. They are not based on empirical data, and there is clearly an urgent need to collect the information that can be used to provide more realistic estimates of the parameters that define these relationships.

Experts were asked to provide a set of values that would define a statistical distribution for the number of days of disturbance that a pregnant female CIBW could tolerate in the period April, May and June before there will be a reduction in her energy reserves when she gives birth. The experts firstly agreed that a plausible range for this parameter was 0-45 days. Figure 8 shows the subsequent statistical distributions chosen by each of the experts, based on their selection of a mean, lower quartile and upper quartile value (see Appendix 3).

Figure 8 - Statistical distributions chosen by each expert in answer to the question, “How many days of disturbance (x-axis) can a pregnant female CIBW tolerate in the period April, May and June before there will be a reduction in her energy reserves when she gives birth?” The group average (linear pool) distribution is shown by the thick dashed red line.


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Figure 9 - Group consensus distribution (black line) and group average distribution (red line) derived from answers to the question, “How many days of disturbance (x-axis) can a pregnant female CIBW tolerate in the period April, May and June before there will be a reduction in her energy reserves when she gives birth?”

A group discussion was undertaken to achieve a consensus distribution, which is shown in Fig. 9. The consensus distribution has a median value of 16 days of disturbance with an inter-quartile range of 9 – 22 days. The experts were then asked to provide a set of values that would define a statistical distribution for the number of days of disturbance in the period April, May, and June that would be required to reduce the energy reserves of a pregnant CIBW to such a level that she is certain to terminate the pregnancy or abandon the calf soon after birth. The experts agreed that a plausible range for this parameter was 5-75 days. Figure 10 shows the distributions chosen by each expert and the group average.


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Figure 10 - Statistical distributions chosen by each expert in answer to the question, “How many days of disturbance (x-axis) in the period April, May and June would be required to reduce the energy reserves of a pregnant CIBW to such a level that she is certain to terminate the pregnancy or abandon the calf soon after birth?” The group average (linear pool) distribution is shown by the thick dashed red line.

Figure 11 - Group consensus distribution (black line) and group average distribution (red line) in response to the question, “How many days of disturbance (x-axis) in the period April, May and June would be required to reduce the energy reserves of a pregnant CIBW to such a level that she is certain to terminate the pregnancy or abandon the calf soon after birth?”

A group discussion was undertaken to achieve a consensus distribution, which is shown in Figure 11. This has a median value of 43 days and an inter-quartile range of 27 – 53 days.

10 20 30 40 50 60 70

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Scaled Beta( 1.73 , 1.72 )

mean = 40.1 , sd = 16.6

median = 43 , LQ = 27 , UQ = 53


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2.4.3 Mechanism 2: Lactating females fail to gain sufficient energy by the end of summer to maintain themselves and their calves during the subsequent winter

It should be recognized that all of these estimates are based either on strong assumptions or on the opinions of the experts we consulted. They are not based on empirical data, and there is clearly an urgent need to collect the information that can be used to provide more realistic estimates of the parameters that define these relationships.

Experts were asked to provide a set of values that would define a statistical distribution for the number of days of disturbance that a lactating female CIBW could tolerate in the period April-September before her energy reserves will be insufficient to maintain her and her calf through the winter. The experts agreed that a plausible range was 15-108 days. Figure 12 shows the distributions chosen by each expert and the group average.

Figure 12 - Statistical distributions chosen by each expert in answer to the question, “How many days of disturbance (x-axis) can a female CIBW who is lactating tolerate in the period April-September before her energy reserves at the end of September will be insufficient to maintain her and her calf through the winter?” The group average (linear pool) distribution is shown by the thick dashed red line.


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Figure 13 - Group consensus distribution (black line) and group average distribution (red line) agreed in response to the question, “How many days of disturbance (x-axis) can a female CIBW who is lactating tolerate in the period April-September before her energy reserves at the end of September will be insufficient to maintain her and her calf through the winter?”

A group discussion was undertaken to obtain a consensus distribution, which is shown in Figure 13. That distribution has a median value of 39 days and an inter-quartile range of 33 – 58 days. Experts were then asked to provide a set of values that would define a statistical distribution for the number of days of disturbance in the period April-September required to reduce the energy reserves of a lactating CIBW to a level where she is certain to abandon her calf. The experts agreed that a plausible range for this parameter was 30-120 days. Figure 14 shows the distributions chosen by each expert and the group average.

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Scaled Beta( 1.93 , 3.73 )

mean = 46.7 , sd = 17.1

median = 39 , LQ = 33 , UQ = 58


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Figure 14 - Statistical distributions chosen by each expert in answer to the question, “How many days of disturbance (x-axis) in the period April-September would be required to reduce the energy reserves of a lactating CIBW to a level that she is certain to abandon her calf?” The group average distribution is shown by the thick dashed red line.

Figure 15 - Group consensus distribution (black line) and group average distribution (red line) in response to the question, “How many days of disturbance (x-axis) in the period April-September would be required to reduce the energy reserves of a lactating CIBW to a level that she is certain to abandon her calf?”

A group discussion was held to explore and agree upon a consensus distribution, which is shown in Figure 15. This distribution has a median value of 69 days and an inter-quartile range of 55 – 92 days.

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Scaled Beta( 1.43 , 1.52 )

mean = 73.6 , sd = 22.6

median = 69 , LQ = 55 , UQ = 92


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2.4.4 Linking body condition and vital rates In order to incorporate the relationships between the number of days of disturbance experienced by a female CIBW and her energy reserves developed during the workshop into the iPCoD framework, it is necessary to include a hypothetical relationship between energy reserves and the probability of giving birth (for pregnant females) or calf survival (for lactating females). The experts discussed the merits of the hypothetical relationships between energy reserves and vital rates proposed by Nabe-Nielsen et al. (2013) and Cairns (1988). However, they concluded that, in the absence of any empirical evidence on the form of this relationship for beluga whales, it was most parsimonious to assume a 1:1 relationship between the predicted change in energy reserves and vital rates (e.g., a 10% reduction in the energy reserves of a pregnant female will result in a 10% reduction in the probability of giving birth). The interim PCoD framework captures the variation among experts in their predictions about the shape of the relationship between the number of days of disturbance experienced by a female CIBW and either her probability of giving birth or the survival rate of her calf by creating predictions for 100s of “virtual” experts. These predictions are obtained by sampling at random from the consensus statistical distributions for the parameters A and B in Figure 4. Because the statistical distributions for A and B were obtained independently, it is possible to sample a value for B that is smaller than the sample value for A. This is clearly unrealistic since it implies that less disturbance is required to reduce the vital rate to zero than is required to have any effect. To avoid these unrealistic combinations, we rejected any pairs of values where (B-A) was less than 2 days. In future, this problem could be avoided by asking experts to specify the number of extra days of disturbance required to reduce a vital rate to zero (see section 2.7). Figures 16 and 17 show the predicted relationships between the number of days of disturbance experienced by a pregnant or lactating female CIBW and the effect of that disturbance on her probability of giving birth and the survival of her calf based on the assumption that there is a linear relationship between energy reserves and these vital rates for 250 “virtual” experts.


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Figure 16 - The predictions of 250 “virtual” experts of the relationship between the number of days of disturbance experienced by a pregnant female CIBW in April, May and June and the probability that she will give birth to her calf. The density of the lines provides an indication of how frequently particular combinations of parameter values are likely to be chosen.

Figure 17 - The predictions of 250 “virtual” experts of the relationship between the number of days of disturbance experienced between April and September by a pregnant or lactating female CIBW and the survival rate of her calf. The density of the lines provides an indication of how frequently particular combinations of parameter values are likely to be chosen. The horizontal line represents situations where the “virtual” expert predicts that disturbance will have no effect on the baseline calf survival rate of 0.9.


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2.5 Incorporating other noise effect mechanisms into interim PCOD or PVA models Table 3 summarises the results of group discussions about how the noise effect mechanisms that were not investigated as part of this expert elicitation process might be incorporated into interim PCoD and other population models, such as Hobbs et al. (2016).

Table 3. Ways in which the noise effect mechanisms not considered as part of the expert elicitation could be incorporated into population models

Noise effect mechanism Potential ways to incorporate the mechanism into population models

3. High levels of stress caused by chronic exposure to noise interact with masking and other disturbance events.

Chronic effects of this kind are difficult to incorporate into interim PCoD. However, the effects of stress could be incorporated by reducing the number of days of disturbance required to reduce vital rates, or by a general reduction in vital rates. However, experts pointed out that this mechanism attempts to capture the effects of continuous chronic exposure, and that it might be more appropriate to model these effects as an overall increase in daily energy requirements.

4. Non-pregnant females fail to gain enough energy to become pregnant in the following spring.

This mechanism could be modelled using the results from an additional expert elicitation.

5. Mother-calf pairs are separated by a disturbance event and cannot restore connection (e.g. because of masking).

Rare events, such as this, that can have acute effects are difficult to assess using expert elicitation. However, the workshop noted that Vergara (in attendance) is undertaking new studies on the frequency of these events. Once this has been quantified, they could be included in the existing PVA model.

6. Animals fail to detect approaching killer whale groups as a result of PTS from historical noise exposure, TTS and/or distraction and masking from current noise exposure.

Data on the frequency of these events might be obtained from long-term monitoring studies of killer whale occurrence in Cook Inlet. The workshop noted that although transient killer whales are silent, beluga whales may use sonar to detect approaching predators, and this ability could be compromised by PTS, TTS, distraction or masking.

7. Strandings as a response to acute sound exposure or reduced ability to navigate because of PTS or masking of echolocation signals.

Another low probability/high consequence event. Hard to model explicitly, because small changes in probability can have large effects. Stranding risk is already included in the PVA model.

8. Reduced energy intake during winter because of masking* and displacement due to noise.

This mechanism could be modelled using the results from an additional expert elicitation. This elicitation would need to account for potential climate-driven changes in ice coverage during winter.

*exacerbated by the energetic costs of the Lombard effect


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2.6 Research that could improve understanding of the effects of noise on CI beluga whales In the final session of the workshop, the experts considered what additional research might improve understanding of the effects of noise on CIBW, while noting that NOAA (2015) and the series of “Species in the Spotlight” reports (see http://bit.ly/1WeNJmq) have already identified a set of overarching research priorities for this topic. The following research topics were highlighted by different members of the group:

Basic information on changes in CIBW feeding ecology through the year (what are they eating, when and where?);

Changes in density and distribution of CIBW during the winter months from tagging and passive acoustic monitoring studies;

Temporal and spatial variation in use of river mouths by CIBW;

Integrating the different ecological datasets on CIBW;

Studies of how masking affects feeding success and detection of contact calls by neonates;

Studies to understand the synergistic effects of noise and other stressors;

Studies of changes in the thickness of the blubber layer of captive animals during lactation to link with data from strandings;

Compilation of physiological/bioenergetics information to better inform the mechanisms identified in Table 2 (e.g., bioenergetics model for pregnant and lactating females, compensatory meal size and energy absorption limitations);

Use of PAM, satellite and DTAGs to understand when/where CIBW are feeding. Tracking or focal follow studies during acute noise events (especially to assess potential displacement);

More acoustic monitoring to better understand spatial and temporal variation in the soundscape across Cook Inlet;

Surveys of fish biomass surveys (prey resource) in winter;

Compilation of information on individual CIBW, including the use of drones to assess body size during live strandings;

Studies of shallow water noise levels associated with anthropogenic activities;

Studies of the use of Cook Inlet by killer whales;

Studies to determine whether level B harassment is actually likely to prevent animals from foraging over a complete tidal cycle, as assumed by iPCoD (see below);

Studies of the way in which individual CIBW use different parts of the Inlet to determine whether there are discrete sub-populations (see below).

http://bit.ly/1WeNJmq


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2.7 Potential improvements to the expert elicitation process A survey questionnaire was distributed after the workshop. The experts’ response to the questionnaire included the following suggestions for improvements to the expert elicitation process:

1. Redefining the questions used in the expert elicitation to ask for the difference between A and B in Figure 4 rather than the absolute value of B.

2. Providing more background information on what is known about beluga whales (e.g., physiology,

lactation, abortion rates, nutritional requirements, body condition), with most attention relating

to bioenergetics.

3. Providing summary tables and diagrams illustrating factors that may influence the quantities/parameters of interest.

4. Providing tools to better visualize the resultant distribution from choice of median and inter-quartile range, e.g., a Shiny visualization app (Chang et al. 2015).

5. Undertaking separate elicitations of the effects of exposure to continuous and discrete noise events.

3. Incorporating the expert elicitation workshop results into an interim PCoD framework

The existing iPCoD software, as described in Harwood et al. (2014), was modified to incorporate the mechanisms agreed at the expert elicitation that relate: 1) the amount of disturbance experienced by an adult female CIBW in the period April-June to its birth rate; and 2) the amount of disturbance experienced between April and September to the survival of its calf. Following the approach developed in Hobbs et al. (2016), we model the population dynamics of CIBW as a birth-pulse process, with all births assumed to occur on 1 July each year. We have provided a set of demographic rates for the CIBW population that result in a population trajectory that, in the short term, closely matches that predicted by the model of Hobbs et al. (2016). However, the user can easily modify these values if new information on demographic rates becomes available. Each noise source that might cause disturbance to CIBWs is modelled as a separate “Operation”. The user must specify how many Operations are to be modelled, the temporal pattern of the activities that will occur as part of each Operation, and the total number of years over which these Operations will take place. This information is documented in a spreadsheet that provides a calendar of activity for each of the Operations in *.csv format. A template for this is included with the iPCoD for CIBW software. Although the calendar specifies the days of each year on which activities will occur, the precise timing of these activities is not critical to the predictions of iPCoD (i.e., a difference of a few days in the timing of activities will have no effect). It is, however, important that the proportion of total activities that occur in April-June is realistic, because it is these activities that will affect the probability that pregnant females will give birth. Similarly, the proportion of activities specified to occur between April and September needs to be accurate, because only these will affect calf survival.


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iPCoD calculates the probability that an individual CIBW will experience Level B harassment on each day that an activity is specified in the calendar. In order for this calculation to be carried out, the user must specify the average number of animals predicted to experience Level B harassment as a result of 1 day of activity for each Operation. This does not have to be a whole number, and may be less than 1 in situations where the predicted density of animals around the location of an Operation is relatively low. We assume that any animal that experiences Level B harassment on a particular day will lose the ability to forage for at least one tidal cycle (i.e. it forgoes 50-100% of its energy intake on that day). The user must also specify the total size of the CIBW population at the start of each simulation, the number of years to be simulated, and the number of simulations to be conducted for the scenario that is being investigated. We recommend a minimum of 500 simulations be run for any scenario. It is possible to specify that only a proportion of the total CIBW population will be exposed to disturbance from a specific Operation, because only these animals spend any time in the immediate vicinity of that Operation. This is referred to as a “vulnerable sub-population”, and the user must specify the relative size of this sub-population (as a proportion of the total population). We recognise that there is currently insufficient information on the way individual CIBWs use the waters of Cook Inlet throughout the year to identify any vulnerable sub-populations. However, King et al. (2015) found that the potential effects of offshore wind farm construction on harbor porpoises in the North Sea was much greater if the total population was divided into a number of different vulnerable sub-populations, each of which experienced different levels of disturbance. We therefore assumed that it would be useful to retain the ability to model population structure in this way in the version of iPCoD developed for CIBW. If more than one vulnerable sub-population is specified, the user must identify which Operations are likely to affect each sub-population. It is important to ensure that the number of animals in a sub-population is greater than the total number of animals predicted to experience Level B harassment as a result of 1 day of activity for all of the Operations that may affect that sub-population. The software will generate an error message if this is not the case. Newly developed CIBW-specific iPCoD code (R-based) and a “How to guide” was provided to NOAA as part of this workshop’s deliverables.


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4. An illustrative application of the CI beluga whale interim PCoD framework

To illustrate how the version of the iPCoD software described in section 3 might be used to evaluate the potential effects of noise associated with a specific industrial development on CIBW, we invented a construction scenario that involved four different piling Operations taking place over a 4 year period. The following number of animals were assumed to experience Level B harassment on each day of piling activity:

Operation 1 0.2 animals/day



Operation 4 0.25 animals/day We assumed the following pattern of activities, with all piling taking place between 1 April and 31 July:

Year Operation 1 Operation 2 Operation 3 Operation 4

1 45 days - 42 days -

2 - - 90 days 54 days

3 - - 42 days 96 days

4 - 45 days - 24 days

In addition to the outputs described in section 1.1 (a time series of predicted CIBW vital rates during and immediately after the construction period that can be incorporated into the existing Hobbs et al. 2016 PVA), we believe it would be useful to know if a particular construction scenario is likely to have any effect on the CIBW population. We therefore determined how many simulations for a particular scenario were likely to result in any animal experiencing more days of disturbance than the lower limit of the plausible range agreed by the experts. That is, any disturbance between April and June for pregnant females, and 15 days of disturbance between April and September for pregnant females and females with calves. We refer to this as a threshold level of disturbance. Under this scenario, no females were predicted to experience more than the threshold level of disturbance for an affect on calf survival, and so no effects on calf survival were predicted. Some pregnant females were predicted to experience more than the threshold level of disturbance in every simulation. However, this was only sufficient to have a predicted effect on the probability of giving birth in one out of 500 simulations, and only one female was affected in this simulation. The mean population birth rate during the years when disturbance occurred was reduced by a maximum of 0.3% (from 0.1666 to 0.1662). We then examined a theoretical scenario in which only 20% of the CIBW population was vulnerable to disturbance from these hypothetical Operations. Again, no females were predicted to experience more than the threshold level of disturbance for an affect on calf survival. Pregnant females were predicted to experience sufficient disturbance to reduce their birth rate in 3 out of 500 simulations, but the effects on mean population birth rate in these simulations were even smaller than those predicted under the previous scenario.


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In another hypothetical scenario, we increased the total behavioral take for the entire construction period to 200 animals (compared with the total behavioral take of 100 associated with the original scenario) and assumed that 20% of the population was vulnerable to this disturbance. Under this scenario, there was a predicted effect on the birth rate of at least 1 female in 11 out of the 500 simulations. However, the predicted reduction in mean birth rate in these simulations was still very small (less than 0.5%). Finally, we explored the effect of further increasing the total number of behavioral takes over the entire period of construction to 500 animals. This increased the number of simulations in which at least 1 female was predicted to experience a reduction in birth rate to 20 out of 500 simulations, but the mean effect on population birth rate in these simulations was similar to that predicted in the other scenarios (i.e., small). Under all of the construction scenarios we examined in this purely hypothetical example, the effect of disturbance on vital rates, when it was predicted to occur, was so small that it would likely not warrant conducting a full PVA with the simulated predictions. The iPCoD software for CIBW can be used to investigate the effects of these predicted changes in birth rate on population size over short time periods (<10 years). For example, in the worst case scenarios described above, the CIBW population was predicted to be on average 6 animals smaller than an equivalent undisturbed population at the end of the construction period in the 20 simulations in which an effect of disturbance on birth rate was predicted. However, if projections of the possible longer-term effects of disturbance are required, the PVA model developed by Hobbs et al. (2016) should ideally be used because it takes account of factors, such as density dependence, which allow the population to recover from the temporary effects of disturbance. These are not presently included in iPCoD.


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5. Appendix 1 - Workshop Participants, Roles and Declarations of Interest/expertise

Name Affiliation Role Declaration of interests Participants expertise

John Harwood CREEM, University of St. Andrews

PCoD Scientist

Vested interest in PCoD Snow geese in Canadian Arctic, marine mammal population biology and behavioral ecology, PCoD.

Dominic Tollit SMRU Consulting NA

Project manager

Developing transparent and robust methods to calculate takes and determine population level effects using interim PCoD approaches in impact risk assessments

Pinniped foraging ecologist, noise effects, acoustic monitoring and risk assessment frameworks using iPCoD

Cormac Booth SMRU Consulting UK

Project support

Transferring knowledge from science to management, useful tools, research

Cetacean ecology, distribution, modeling survey data, PCoD

Jason Wood SMRU Consulting NA

Rapporteur Interest in applying research to management/mitigation and impact assessment

Acoustics, terrestrial then marine, impact assessments, sat in on several PCoD EE workshops and used simplified PCoD for SRKW assessment, acoustic impact assessments

Leslie New Washington State University

Facilitator Interest in research on EE and PCoD PCoD framework, EE facilitation

Len Thomas CREEM, University of St. Andrews

EE Analyst Research on EE and PCoD Population estimates, PCoD, stats, cumulative effects

Rodd Hobbs NOAA – NMML

Host CIB recovery Leader of CIB at NNML, CIB since 94, population assessment and modeling

Manuel Castellote NOAA – NMML

Host move forward from behavioral changes due to noise to population level effects

Acoustics, fin whales, CIB, acoustic behavior, acoustics as a tool for habitat use, effects of noise on CIB, behavioral reaction of CIB to noise

Jason Gedamke NOAA - OST Funder Government employee and scientist, cumulative effects

Underwater sound, ocean acoustics manager (start in ATOC in 90s), esp. large whales


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Jolie Harrison NOAA - OPR Funder Converting takes to survivorship and population effects

Biologist, OPR, Chief Permits Division

Dawn Noren NOAA - NWFSC

Applied research

Participant at ONR PCAD workshop as well as an IWC workshop on determining the biological significance of whale watching effects on large whales. SRKW expertise

Francine Kershaw NRDC Expand spatial application of PCoD tools for mitigation, research, conservation management perspective

Post Doc, NRDC NY, geospatial analysis, population genetics baleen whales

Kenneth Tarbox ADFG (ret) General interests Commercial fisheries in Cook Inlet (esp. salmon), watching CIB for decades

Marla Holt NOAA - NWSFC

Research perspective, endangered species Pinniped acoustics, hearing, effects of noise, SRKW,

Robert Michaud GREMM Science based tools for management, can it be applied to St Laurence Beluga

Beluga in St. Laurence, St Laurence Beluga recovery team member, long term photo ID studies of beluga

Robert Small ADFG Synthesizing expertize in room to provide guidance to regulators, guide research priorities

Wildlife scientist, pop ecology, 25 years ago switched to marine, CIB habitat use (via acoustics)

Robert Suydam North Slope Borough

Works with AK hunters, cumulative effects Wildlife biologist, marine mammals, bowhead, beluga. Started in ornithology

Sam Simmons MMC Scientist at MMC (Identification of gaps in science), regulatory implications

Pinniped biologist, PCoD experience (elephant seals and onwards), stock assessments in Alaska

Tamara McGuire LGL Research, cumulative effects

Wildlife biologist with LGL, 10-year photo id on CIB, social structure, movements, population, CIB recovery team

Tiffini Brookens MMC Converting takes to survivorship, and how that translates into MMPA negligible take determination

Biologist, review IHA and provide comments


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Valeria Vergara Vancouver Aquarium

Effects of noise on marine mammals Behavioral ecology, terrestrial mammals at start, 2002 started on Beluga, acoustics, mother calf acoustic contact

Veronique Lesage DFO, Canada

Frameworks for evaluating impacts of marine developments on marine mammals, how PCoD fits into this framework, esp. for St Laurence Beluga

Beluga research for 25 years, noise impacts on St Laurence Beluga, seals, foraging ecology, 15 years with DFO, large whale ecology

Greg Balogh NOAA - PRD

NOAA Fisheries, identify knowledge gaps to help inform our funding prioritization, turning take numbers to population (and individual) impacts (for Section 7 consultation) and an aid in recovery task prioritization.

ESA, CIBW


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6. Appendix 2 - Workshop Agenda The workshop was held on 26-28 April 2016 in the Traynor Room, Building 4, AFSC, NOAA 7600 Sand Point Way NE, Seattle, WA 98115. Day 1 Agenda

Time Agenda

9:00 Workshop overview and goals – Tollit

9:15-9:45 Overview of PCoD and Interim PCoD frameworks – Harwood

9:45-11:00 Overview of NMFS research on Cook Inlet beluga (population abundance, distribution, and acoustic monitoring) - Hobbs & Castellote

11:15-12:00 Regulatory perspective – Balogh & Small

12:00-12:30 Spatial distribution of noise effects in Cook Inlet - McGuire

1:30-3:45 Overview of potential noise-related threats and identify how noise-related threats might affect vital rates – Noise Effect Mechanisms

4:00-5:00 Identify best ways to model noise-related effects within an Interim PCoD framework

Day 2 Agenda

Time Agenda

9:00-10:00 Selection of Noise Effect Mechanisms for Expert Elicitation

10:00-11:00 Elicitation – calibration question

11:15-12:15 Elicitation – calibration question - Results

1:15-5:00 Elicitation - 1st Noise Effect Mechanism – definition of Parameter A question, group consensus on upper and lower bounds, defining quartiles


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Day 3 Agenda

Time Agenda

8:40-9:15 Elicitation - 1st Noise Effect Mechanism – Results (Parameter A) and group feedback, Consensus on Parameter A

9:15-9:45 Elicitation - 1st Noise Effect Mechanism – definition of Parameter B question, group consensus on upper and lower bounds, defining quartiles

10:00-10:30 Elicitation - 1st Noise Effect Mechanism – Results (Parameter B) and group feedback, Consensus on Parameter B

10:30-11:15 Elicitation - 2nd Noise Effect Mechanism – definition of Parameter A question, group consensus on upper and lower bounds, defining quartiles

11:15-12:15 Elicitation - 2nd Noise Effect Mechanism – Results (Parameter A) and group feedback, Consensus on Parameter A

12:15-1:15 Elicitation - 2nd Noise Effect Mechanism – definition of Parameter B question, group consensus on upper and lower bounds, defining quartiles

1:45-2:15 Elicitation - 2nd Noise Effect Mechanism – Results (Parameter B) and group feedback, Consensus on Parameter B

2:15-2:35 Parameterize the curves linking Energy Reserves to Fitness (the probability of immediate calf survival)

2:35-3:35 Revisit remaining Mechanisms and discuss research requirements for better understanding each Mechanism

3:35-3:45 Plans to incorporate Mechanisms 2 and 4 into current PVA using interim PCoD and dissemination

3:45 Workshop close


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7. Appendix 3 - Elicitation process

The elicitation was conducted using the Sheffield Elicitation Framework (SHELF V2.0), based on

elicitation practice recommended in O’Hagan e t al (2006). Protocols for eliciting a distribution were

based on the Quartile (Q) method. Experts were given training in the process of elicitation at the start

of the meeting, this included an explanation of probability and a practice exercise to familiarize experts

with the procedure. This is termed the calibration elicitation. It is important to note that experts were

not asked to provide single estimates of any quantities. The elicitation process instead involves

considerations such as what a plausible range of values would be for each unknown quantity, and

whether, in each person’s opinion, some values are more likely than others. Experts may have

considerable uncertainty about some of these quantities (though less than that of a layperson). This

will not be of concern during the elicitation itself, as the outputs from the elicitation will reflect large

uncertainty when it is present. In addition, the method uses a consensus approach that takes into

account research which has shown that the reliability of results from an expert elicitation process can

be improved if experts are asked to consider their opinions in the light of what other experts have said

(Burgman et al. 2011). This is known as the Delphi process (Delbecq et al. 1975).

The SHELF (Quartile method) process can be summarized as follows

1) Provide briefing documents to experts prior to the meeting

2) Identify the quantities whose probability distributions are to be elicited.

3) Review any specific evidence about each mechanism.

4) Elicit the following values in turn:

i. The range of plausible values for the mechanism, such that, L is the lower bound of the

range and U the upper bound. It may be useful to record absolute, logical bounds, but the

objective here is to identify a range such that it is extremely unlikely (but not necessarily

impossible) that the value for the mechanism (hence termed X) lies outside the range. As a

group, record how the range has been informed by the evidence base. (Note that this range

should not be unnecessarily wide, but it is important that it should not be too narrow. This

should be a joint judgement of the experts, such that they all believe that X is extremely

unlikely to be outside (U, L). After an initial specification of the range, the facilitator will

probe by asking something like “Suppose an experiment produced a value [something below

L or above U] for X; would this have to have been a flawed experiment, or might there be a

way that X could have this value?”)

ii. The next step is done by each expert separately, without discussion. Each expert is asked to

specify their median value for the mechanism. This is a value such that they think “X lies

below the median” and “X lies above the median‟ are equally likely propositions. Formally,

if M is the median, then P(X < M) = 0.5. (The facilitator should instruct the experts to write


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down their own median values, but not to reveal them yet. Nothing should be written in this

field until after the upper and lower quartiles have also been elicited).

iii. Each expert is then asked to specify their lower quartile by considering the range from L to

M and dividing it into two equally likely intervals. Formally, if Q1 is the lower quartile, P(L <

X < Q1) = P(Q1 < X < M) = 0.25. Similarly, each expert should specify their upper quartile Q3

by dividing the range from M to U into equally likely intervals. Then P(M < X < Q3) = P(Q3 <

X < U) = 0.25. Before deciding definitely on these values, experts are asked to check that

they regard each of the four ranges (L to Q1, Q1 to M, M to Q3 and Q3 to U) as equally likely.

iv. When all the experts have written down their medians and quartiles, the facilitator will

collect these values. The analyst then fits a distribution to each of the expert’s assessments.

The analysts will choose an appropriate family of distributions, and then fit the distribution

by choosing parameters that give probabilities matching the elicited bounds, median and

quartiles as closely as possible. Since L and U are not necessarily absolute bounds, there are

6 probabilities to match – four of 0.25 and two of 0.0.

v. The distributions are then shown to the experts, but at this stage no revision is made (unless

an expert is insistent that the plotted distribution badly distorts his/her beliefs) or provide

any other feedback. The analyst will then compute and reveal the median and quartiles of

an equally-weighted average of the density functions.

vi. After discussion of the different distributions, and sharing of knowledge and reasoning

about the differences, the group consensus values for probabilities P1 = P(L < X < X1) and

P2 = P(X2 < X <U), and finally for P0 = P(L < X < X0), where X1, X2 and X0 are selected by the

facilitator are recorded. The agreed median will inevitably be some sort of compromise.

Before discussion, there are two components of uncertainty in the group – uncertainty that

each expert has and is expressed in that expert’s quartiles, as well as variability between the

experts‟ judgements. The agreed probabilities should reflect the group’s overall uncertainty

that remains after the discussion.

vii. Record the (potentially iterative) process of fitting, feedback and revision of the group

judgements. (The analyst first fits a distribution to the group’s probability values. This should

be shown to the experts, and the fitted probabilities compared with the elicited

probabilities. The experts are invited to consider whether the fit is close enough, or whether

some values might be varied in order to fit others (that are believed to be more pivotal)

better. The facilitator then feeds back to the experts some implied probabilities in the fitted

distribution, such as the 10th and 90th percentiles, or the median and quartiles. The experts

are invited to consider whether these are reasonable reflections of the group’s knowledge.

If revision is needed, this may be followed by further rounds of fitting and feedback until the

experts are comfortable with the fitted distribution and its implications).

viii. The facilitator then records any difficulties that arose during the elicitation of this

distribution, and the experts’ reactions to the process and to the final fitted distribution.


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Due to the subjective nature of elicited probability distributions, it is important to make the elicitation process as transparent as possible. A written record is kept of the meeting, which includes details of experts present at the meeting, a summary of each expert’s relevant expertise, and any declarations of interest (Appendix 1). Declarations of interest are recorded for the purposes of transparency only, and will not be used as grounds for exclusion from the elicitation. Briefing documents (Appendix 4) were supplied to all experts prior to the workshop, together with a variety of relevant papers and reports. These papers and reports can be found at https://smrumarine.app.box.com/files/0/f/7472178557/BW_PCoD_reading_material

https://smrumarine.app.box.com/files/0/f/7472178557/BW_PCoD_reading_material


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8. Appendix 4 - Briefing documents provided to experts before workshop

COOK INLET BELUGAS: POPULATION CONSEQUENCES OF DISTURBANCE WORKSHOP

BACKGROUND INFORMATION FOR EXPERT ELICITATION Dominic Tollit1, Cormac Booth1, John Harwood2, Len Thomas2 & Jason Wood1

1. SMRU Consulting & SMRU LLC, 2. CREEM, University of St Andrews Introduction The Cook Inlet (CI) beluga whale (Delphinapterus leucas) population declined from around 1,300 whales in 1979 to 367 in 1999 (Hobbs et al. 2000). Alaskan Native subsistence harvest between 1993 and 1998 ranged from 21 in 1994 to 123 in 1996 with the most reliable data coming from 1995 – 1997 when an average of 87 whales were taken per year over that period (Angliss and Lodge, 2002). The subsistence take was sufficient to account for most of the observed decline in the beluga population over this period. Alaskan Natives imposed a voluntary moratorium in 1999, and in 2000 NMFS declared the population depleted under the Marine Mammal Protection Act (65 FR 34590). Since 1999 the total subsistence harvest has been five whales, with none taken after 2005 (NMFS, 2015). Nonetheless, the population has continued to decline (Fig. 1). The most recent estimate is 340 in 2014 (Shelden et al. 2015).

Figure 1. FIGURE 13 from NMFS (2015) showing abundance estimates for Cook Inlet beluga whales

between 1994 and 2014. Vertical bars indicate the 95% Confidence Interval for each estimate.

The 10 year trend from 2004 to 2014 was -0.4% per year (SE = 1.3%).


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The core summer distribution of the population contracted from over 7,000 km2 in 1978-79 to 2,800 km2 in 1998-2008 (Rugh et al. 2010). As a result, most of the population is concentrated in upper Cook Inlet, close to the port of Anchorage, during the summer months, where it is most likely to be exposed to disturbance from human activities (NMFS, 2015). The draft recovery plan for the CI beluga whale population (NMFS, 2015) lists 10 threats that could be contributing to the non-recovery, and identifies three as being of high relative concern. Those threats are: 1. catastrophic events (e.g. natural disasters, oil spills, mass stranding), 2. cumulative and synergistic effects of multiple stressors (primarily between noise, non-biological

toxins and perceived threats) 3. noise In this workshop we will investigate how noise-related stressors might affect vital rates (survival, birth rate and growth) for CI beluga and the best way to model those effects. NMFS (2015, section IX.D - CI Beluga Hearing, Vocalization, and Noise Supplement, see Appendix 1) suggests that the main direct effects of noise on CI belugas are likely to be through masking of vocalizations used for communication and prey location, and habitat degradation. Changes in the vocal behavior that may compensate for the effects of masking have been recorded for belugas in the St Lawrence Estuary (Sheifele et al. 2005), but such changes are likely to incur energetic costs (Holt et al. 2015). Belugas in the Arctic have also been observed to temporarily vacate areas in response to pulsed sound from seismic surveys and ice-breaker noise (see Appendix 1 of NMFS 2015). In addition, there may be synergistic effects between noise and certain chemical pollutants, although NMFS (2015) notes that “these synergistic effects have not yet been described in marine mammals”. PCAD and PCoD In 2005 a panel convened by the National Research Council of the United States National Academy of Sciences published a report on “Marine Mammal Populations and Ocean Noise: Determining When Noise Causes Biologically Significant Effects”. The panel developed what it called “a conceptual model” that outlined how marine mammal might be affected by anthropogenic noise and how population level effects could be inferred on the basis of observed behavioral changes. They called this model Population Consequences of Acoustic Disturbance (PCAD; Fig. 2). In 2009 the US Office of Naval Research (ONR) set up a working group to transform this framework into a formal mathematical structure and determine how that structure could be parameterised using data from a number of case studies. The ONR working group extended the PCAD framework so that it could be used to consider other forms of disturbance and to address the impact of disturbance on physiology as well as behavior. The current version of that framework is now known as PCoD (Population Consequences of Disturbance); it is shown in Figure 3a and described in more detail in New et al. (2014).


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Figure 2. The Population Consequences of Acoustic Disturbance (PCAD) model developed by the National

Research Council’s panel on the biologically significant effects of noise. After Fig. 3.1 in NRC (2005). The

number of + signs indicates the panel’s evaluation of the relative level of scientific knowledge about the links

between boxes, 0 indicates no knowledge. These links were described by the panel as “transfer functions”.

The PCoD framework illustrates how disturbance may impact both the behavior and physiology of an individual, and how changes in these variables may affect that individual’s vital rates (its probability of survival and of producing an offspring) either directly (an acute effect) or indirectly via its health (a chronic effect). For example, behavioral changes in response to disturbance may have an acute effect on survival if they result in a calf being separated from its mother, or if they affect an individual’s ability to recover from the effects of deep dives, increasing its risk of decompression stress (Hooker et al. 2012). They may have chronic effects on reproduction via body condition if disturbed animal spending less time feeding or in activities, like resting, that conserve energy.


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Figure 3a. A conceptual model of the population consequences of disturbance (PCoD) developed by the

ONR Working Group on PCAD (modified from Fig.4 of New et al. 2014). The term “health” is used

to describe all aspects of the internal state of an individual that might affect its fitness. These could

include, for example, the extent of its lipid reserves or its resistance to disease. “Vital rates” refers

to all the components of individual fitness (probability of survival and producing offspring, growth

rate, and offspring survival). The arrows represent transfer function, which describe the

mathematical relationships between the boxes that they link.

Figure 3b. The simplified version of the ONR working group framework used in the interim PCoD

protocol. The transfer functions that determine the chronic effects of physiological and behavioral

change on vital rates are represented with dotted lines to indicate that their mathematical form is

determined using the results of an expert elicitation process rather than empirical evidence.


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This project We will incorporate the relationships between exposure to noise-related stressors and beluga vital rates developed at this workshop into the Interim PCoD framework (Fig. 3b, King et al. 2015), which is a simplified version of the full PCoD model. The population modelling component of the interim PCoD software (Harwood et al. 2013) will be modified so that it matches the extinction risk model developed for CI belugas by Hobbs et al. (2008). We will use expert elicitation to obtain estimates of the parameters that determine the shape of the relationships between noise exposure and vital rates. Expert elicitation (Runge et al. 2011, Martin et al. 2012), also known as expert judgement (Aspinall 2010), is now widely used in conservation science to combine the opinions of many experts in situations where there is a relative lack of data but an urgent need for conservation or management decisions. It allows information obtained from multiple experts to be combined into quantitative statements which are then incorporated into mathematical models. It also provides methods for minimizing bias in the elicited information and ensuring that uncertainty is accurately captured. One of its main aims is to avoid many of the well documented problems (such as anchoring, confirmation bias and overconfidence, described in detail by Kahneman, 2012) that arise when the judgements of only a few experts are canvassed. Information to be Obtained Using Expert Elicitation King et al. (2015) asked experts to provide parameter values for a set of relationships relating the survival and birth rate of harbor porpoises to the number of days of disturbance they experienced (Fig. 4). However, that information was collected in response to an on-line questionnaire. Recent experience of conducting in-person expert elicitation workshops for beaked whales and sperm whales has suggested that experts find it difficult to provide a sound biological basis for the parameter values of these relationships. For this workshop, we will ask experts to provide values for a similar relationship linking number of days of disturbance to body condition. We will provide detailed guidance on how body condition may be affected by disturbance using examples based on the bioenergetics model for beaked whales developed by New et al. (2013). We will then ask experts to provide appropriate values for a second relationship linking body condition to different vital rates. We will ask experts to choose an appropriate relationship after discussion of the rationale behind the hyperbolic relationship developed by Nabe-Nielsen et al. (2013) for harbor porpoises (Fig. 5), and the sigmoidal relationship (Fig. 6) proposed by Cairns (1988) for seabirds and used for gray whales by Villegas-Amtmann et al. (2016).


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Figure 4. Hypothetical relationship between number of days of disturbance experienced by a harbor

porpoise and its effect on survival or birth rate. After Fig. 2 of King et al. (2015).

Figure 5. The hypothetical relationship between survival probability (s) and energy level (E) used by

Nabe-Nielsen et al. (2013) to model the effects of disturbance on harbor porpoises. The constant β

determines the shape of the survival curve. Taken from Figure A1. of Nabe-Nielsen et al. (2013).

1 ebE

E

S

0 5 10 15 20

0.0

0.2

0.4

0.6

0.8

1.0

b= 0.2

b= 0.4

b= 0.8


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Figure 6. Hypothetical relationship between vital rates and reduction in energy intake for female gray

whales. Taken from Fig. 2 of Villegas-Amtmann et al. (2016).


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APPENDIX D OF DRAFT COOK INLET BELUGA WHALE RECOVERY PLAN (NMFS 2015) – COOK INLET BELUGA HEARING, VOCALIZATION, AND NOISE SUPPLEMENT

NOTE TO READER: The text below was developed by the Cook Inlet Beluga Whale Recovery Team and reproduces

information readily available in other reports. In Sections II.B.6 and III.A.4 of this document, the authors provided information sufficient to justify the recovery criteria and actions. References can be found in NMFS (2015).

1. Beluga Hearing

Having evolved from land based mammalian ancestors, cetaceans have inherited an ear that was first adapted to hearing sounds through air, which then readapted to receiving sounds through water (Thewissen and Hussain 1993). Cetaceans have retained the ear drum, ossicles, Eustachian tube, and middle ear structures, including an air-filled cavity within the temporal bone or bulla, connected by the Eustachian tube to the nasal cavity for equalization of pressure during dives (Gingerich et al. 1983; Thewissen and Hussain 1993; Ridgway et al. 2001). As a consequence, it was hypothesized that cetacean hearing might attenuate at depth due to the increased air pressure and density of air in the middle ear, which might make them less susceptible to the impacts of loud underwater sounds. This has been shown not to be the case in belugas, as their hearing was determined to be as good at 300 m (984 ft.) depth as at the surface (Ridgway et al. 2001). This is consistent with the theory that sound may be received through odontocetes’ lower jaw “acoustic window” and transmitted directly to the ear (Norris 1968; Cranford et al. 2008). In fact, a study conducted with a captive beluga showed that the most efficient hearing pathway is from the rostrum tip, and may indicate that there are acoustic fat channels which begin at the beluga rostrum tip that effectively guide sound to the inner ear (Mooney et al. 2008). To date, belugas are the only odontocetes known to hear from the rostrum tip, although a similar pathway has been recently proposed for Cuvier's beaked whale (Ziphius cavirostris; Cranford et al. 2008). This feature probably gives belugas greater directional hearing abilities than other odontocetes. It is possible that belugas’ unfused vertebrae, which allows for a highly movable head, facilitates increased hearing directionality.

2. Beluga Echolocations and Vocalizations

Belugas utilize an alternative echolocation strategy compared with the bottlenose dolphin when performing identical echolocation tasks (Turl and Penner 1989; Rutenko and Vishnyakov, 2006). Bottlenose dolphins will emit a click and wait until the echo returns before emitting the next signal (i.e., the inter-click interval is always greater than the two-way time travel). Belugas appear to be able to transmit, receive and process signal packets simultaneously, with the first click about two dB higher than the other clicks that follow, which may serve to identify the beginning of each signal packet (Turl and Penner 1989). The first vocal repertoire description of beluga was made in the Canadian high Arctic by Sjare and Smith (1986a). They classified a total of 807 tonal calls (whistles) into 16 contour types and some 436 pulsed calls into three major categories that they describe as “click series”, “pulsed tones”, and “noisy vocalizations.” Subsequent studies have obtained varied results. The vocalizations of adult male beluga groups in Svalbard, Norway were subjectively classified into 21 call types, which were dominated by a variety of whistles (Karlsen et al. 2002). Karlsen et al. (2002) highlighted the highly graded nature of these beluga calls, as one “call type” can merge into another type with very subtle changes, making the classification very challenging. A reproductive gathering of belugas in the White Sea, Russia, has been the subject of several repertoire studies (Belikov and Bel’kovich 2001, 2003; Bel’kovich and Kreichi 2004; Belikov and Bel’kovich 2007, 2008). Whistle-like signals were found to comprise approximately 10% of the total vocal production of this whale group. Of these, 750 signals were divided into 43 classes (Belikov and Bel’kovich 2001) with at least 16 whistle types (Belikov and Bel’kovich, 2007) and vowel-like signals and pulsed signals (Bel’kovich and Kreichi 2004; Belikov and Bel’kovich 2008). The response of a decrease or cessation in acoustic activity has been observed in both captive and free-ranging


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beluga whales (Morgan 1979; Lesage et al. 1999; Karlsen 2002; Van Parijs et al. 2003; Castellote and Fossa 2006) and free-ranging narwhals (Finley 1990) and has been associated with threat, startle, fright, alarm, or stress contexts and interpreted as a survival strategy to avoid detection by predators (Schevill 1964; Fish and Vania 1971; Morgan 1979; Finley 1990; Lesage et al. 1999). A broad band pulsed call labelled “Type A” (Vergara and Barrett-Lennard 2008) was identified as a contact call between mothers and their calves in a captive environment. It is thought that these calls, both in captivity and in the wild, function to maintain group cohesion, and the variants shared by related animals are used for mother-calf recognition (Vergara et al. 2010). The only study on vocal development in belugas suggests that neonates only produce pulse trains before they acquire rudimentary whistles at two weeks of age (Vergara and Barrett-Lennard 2008), although this is based on observations of one captive male beluga calf. However, sound production of another neonate captive beluga whale also consisted exclusively of low-frequency, short duration pulse trains that were not part of the adult’s repertoire (Castellote et al. 2007). Despite differences in populations of origin, captive facilities, health and in acoustic context, the sound production observed in these two neonate whales suggests a species-specific pattern of developmental stages in sound acquisition. Whether these observed captive neonate vocalization characteristics may prove useful in detecting the presence of wild neonates is still to be determined. The most recent study on beluga social signals (Vergara et al. 2010) emphasized the two persistent problems commonly encountered in the study of animal communication: first, the great variability in the physical features of the sounds, with general call types grading into each other (Recchia 1994) introduces great uncertainty in the categorization schemes; secondly, the inherent difficulty in categorizing sounds that are biologically meaningful without testing how belugas themselves perceive or use them (Tyack and Clark 2000). Despite the challenges, some progress has been made in the attempt to correlate vocalization rate and call type with specific beluga behavioral states.

3. Effects to Beluga Hearing and Behavior from Anthropogenic Noise

There is an extensive body of literature regarding the effect of anthropogenic noise on marine mammal behavior. Most of the studies addressing this problem have used behavioral attributes such as changes in site fidelity, dive patterns, swimming speed, orientation of travel, herd cohesiveness, and dive synchrony to indicate possible disturbance or stress caused by noise (Richardson et al. 1995). However, the current knowledge of the effects of anthropogenic noise to marine mammal acoustic behavior is more limited and only a few studies have focused on belugas. Their high auditory sensitivity, wide frequency bandwidth, and dependence upon sound to navigate, communicate, and find prey make belugas vulnerable to noise pollution. Noise pollution may mask beluga signals, or if intense, may lead to temporary or permanent hearing impairment (Awbrey et al. 1988; Finley 1990; Green et al. 1994; Richardson et al. 1995, 1988). Exposure to intense sound can produce an elevated hearing threshold, referred to as a threshold shift (TS). If the threshold later returns to normal it is considered a temporary threshold shift (TTS), but if not, it is considered a permanent threshold shift (PTS). Studies of TTS and PTS have helped to establish noise exposure limits in humans. There are no PTS data for cetaceans, yet a few studies have attempted to establish the TTS for beluga (Finneran et al. 2000, 2002a; Schlundt et al. 2000). Results from one study suggest that beluga whales might be more sensitive than bottlenose dolphins to particular impulsive pressure waveforms (Finneran et al. 2000). Belugas were interpreted as having displayed negative behavioral reactions to water gun impulses. A similar study confirmed that beluga whales may be susceptible to TTS, but that small levels of TTS may be fully recoverable. Finneran et al. (2000) simulated sounds resembling signatures of underwater explosions from 5 or 500 kg HBX-1 charges at ranges from 1.5 to 55.6 km (0.9-34.5 mi), and while the simulated sounds were not intense enough to affect the beluga hearing capabilities, sound levels simulating explosions of 500 kg (1,102 lb) at 1.9 km (1.2 mi) and closer did disrupt the behavior of the belugas. Finneran et al. (2002a) measured hearing thresholds in a


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bottlenose dolphin and a beluga whale before and after exposure to underwater impulsive sounds produced from a seismic water gun. Schlundt et al. (2000) performed a study exposing five bottlenose dolphins and two belugas (same individuals as Finneran’s studies) to intense 1 second tones at different frequencies. The resulting levels of fatiguing stimuli necessary to induce 6 dB or larger masked TTSs were generally between 192 and 201 dB re: 1 microPascal (µPa). Dolphins began to exhibit altered behavior at levels of 178–193 dB re: 1µPa and above; belugas displayed altered behavior at 180– 196 dB re: 1 µPa and above. At the conclusion of the study, all thresholds were at baseline values. Results of this study indicate that at least these two odontocetes species are susceptible to TTS, but that they seem to recover from at least small levels of TTS. A number of studies have examined other characteristics of beluga hearing. Johnson (1991) analyzed hearing thresholds, bandwidths, and integration times (basic descriptive parameters of the cetacean sonar system) for single pulsed tones and multiple pulsed tones of 60 kHz in the presence of noise. He found negative correlations between hearing thresholds and pulse repetition rate with abrupt 5-6 dB steps, and linear correlations between pulse repetition rate and integration times. The author related the abrupt hearing steps to a change in the echolocation strategy based on target distance, as has been described in some beluga whale echolocation studies, and is discussed in the next section. This result, together with a variable integration time and a constant system bandwidth of 1,000 Hz (much lower than previously reported) led the author to suggest that beluga whale sonar systems could not be fully described by a single filter model. In essence, this conclusion was a technical appreciation of the complexity of the beluga whale biosonar system. Finneran et al. (2002c) analyzed beluga sensitivity to acoustic particle motion, which is one of the two physically linked components of sound in water (together with pressure waves), and the main feature detected by all fish species (Fay and Popper 1975). Results suggested that the two beluga whales tested responded to changes in the acoustic pressure alone and were not able to use acoustic particle motion cues. The possibility that noise conditions might mask the ability of animals to hear and decipher specific sounds has been studied in beluga whales in order to understand the potential impacts of anthropogenic noise on belugas. When a tonal signal is played in a broad spectrum of white noise (noise with equal loudness across all frequencies), only the noise energy in a relatively narrow band on either side of the tone frequency is effective in masking the signal, and the rest of the noise spectrum contributes little or nothing to the masking effect. Johnson et al. (1989) analyzed this feature in the hearing of a beluga in a wide frequency range (40–115 kHz) and found that the whale’s ability to detect the signal in noise was slightly better than results previously reported for bottlenose dolphins. Erbe et al. (1999) and Erbe (2000), analyzed the effect of masking of beluga calls by exposing a trained beluga to icebreaker propeller noise, an icebreaker’s bubbler system, and ambient Arctic ice cracking noise, and found that the latter was the least problematic for the whale detecting the calls. Finneran et al. (2002b) analyzed the ability of a beluga whale to detect acoustic signals in noise. A primary feature of the auditory system in these animals is the ability to resolve a complex sound into its individual frequency components by a set of auditory filters, and the filter shape and size affect the loudness and detectability of complex sounds and broadband signals (Scharf 1970). The authors analyzed 20 and 30 kHz pure-tone underwater hearing thresholds in one beluga whale and two bottlenose dolphins in the presence of broadband noise at two intensities: 90 and 105 dB re:1 µPa2 /Hz. Filter shapes obtained for the dolphins and beluga whale were similar, but the filter width was consistently smaller for the beluga whale, conferring better ability to detect acoustic signals in noise. Sheifele et al. (2005) studied a population of belugas in the SLE to determine whether beluga vocalizations showed intensity changes in response to shipping noise. This type of behavior has been observed in humans and is known as the Lombard vocal response (Lombard 1911). Sheifele et al. (2005) demonstrated that shipping noise did cause belugas to vocalize louder. The acoustic behavior of this same population of belugas was studied in the


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presence of ferry and small boat noise. Lesage et al. (1999) described more persistent vocal responses when whales were exposed to the ferry than to the small-boat noise. These included a progressive reduction in calling rate while vessels were approaching, an increase in the repetition of specific calls, and a shift to higher frequency bands used by vocalizing animals when vessels were close to the whales. The authors concluded that these changes, and the reduction in calling rate to almost silence, may reduce communication efficiency which is critical for a species of a gregarious nature. However, the authors also stated that because of the gregarious nature of belugas, this “would not pose a serious problem for intraherd communication” of belugas given the short distance between group members, and concluded a noise source would have to be very close to potentially limit any communication within the beluga group (Lesage et al. 1999). The fact that SLE belugas alter their vocal behavior by increasing the intensity or repetition rate, or by shifting to higher frequencies when exposed to shipping noise (from merchant, whale watching, ferry and small boats) is indicative of an increase of energy costs (Bradbury and Vehrencamp 1998). If noise exposure is chronic, long-term adverse energetic consequences could occur for belugas, as it has been shown for birds (Oberweger and Goller, 2001). Chronic noise exposure could also increase stress levels for CI belugas, as has been shown in northern right whales (Rolland et al. 2012). Definitively linking adverse energetic consequences and chronic stress responses to detrimental health effects in belugas or other cetaceans is extremely difficult because of the logistics of studying free-swimming whales and the inability to conduct a controlled study. However, a large body of literature has demonstrated that chronic stress can lead to detrimental effects on health and reproduction across a variety of vertebrate taxa (Rolland et al. 2012). Both the degradation of the beluga acoustic communication and echolocation space, as well as the noise-induced chronic increase of signalling costs and stress, could lead to negative biological consequences at the population level. Even if these consequences are not yet well understood, there is sufficient evidence to suggest that the reproductive success and survival of cetaceans can be negatively impacted by noise (NRC 2000, 2003, 2005; Cox et al. 2006; Southall et al. 2007; Clark et al. 2009; Payne and Webb 1971; Tyack and Clark 2000). While exhibiting a Lombard response provides a mechanism for animals to cope with varying levels of noise, the need for and use of this response suggests that the animal is attempting to cope with noise levels that are near a point where masking will occur. The effect of shipping noise in the acoustic environment of the endangered SLE beluga was studied recently by Gervaise et al. (2012) in the lower SLE. Noise from a car ferry line as well as a seasonal whale watching fleet were analyzed. The study found both beluga communication and echolocation bands were dramatically affected by these noise sources. Based on the background noise levels, spectra, and periodicity reported, and assuming no behavioral or auditory compensation, beluga communication and echolocation signals could be masked 50% of the time with a reduction of potential communication ranges to less than 30% of their values under natural ambient noise conditions. Similarly, echolocation could be reduced to 80% of their range under natural ambient noise conditions. The study concludes that noise from these sources could easily limit long-range communication (in the order of 1-2 miles [1.6-3.2 km]) among scattered individuals or pods and affect echolocation efficiency in all exposed belugas. There are some documented beluga spatial displacements caused by loud sources of noise. Two different research teams and data from several years showed that belugas typically avoided icebreakers at distances of 35-50 km (22-31 mi), at the point where they could probably just detect them. They travelled up to 80 km (50 mi) from the ship track and usually remained away for 1-2 days (Finley et al. 1990, Cosens and Dueck 1993). When drilling sounds were played to belugas in industry-free areas, the belugas only showed a behavioral reaction when received levels were high (Richardson et al. 1997). Belugas have been observed to show startle responses when drilling noises were played with a received level greater than or equal to 153 dB re 1 μPa. Considerable displacements have also been suggested for noise from air guns typically used during seismic surveys. One seismic survey in the Canadian Beaufort Sea determined behavioral reactions of belugas occurred when two 24 gun arrays of 2,250 in3 were operating (Miller et al. 2005). Results of the analysis of the differences


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between vessel-based and aerial-based beluga sighting distributions provided evidence of reactions of belugas to seismic operations at distances above 20 km (12.4 mi), beyond the effective visual range of the MMOs on the seismic vessel (Miller et al. 2005). Aerial surveys conducted in the southeastern Beaufort Sea in summer found that sighting rates of belugas were significantly lower at distances of 10–20 km compared with 20–30 km from an operating airgun array (Miller et al. 2005). The low number of beluga sightings by marine mammal observers on the vessel seemed to confirm there was a strong avoidance response to the 2250 in3 airgun array; however, it is unclear if the observed movement of the belugas was a direct consequence of the seismic surveys or related to the natural offshore migration at that time of year. More recent seismic monitoring studies in the same area seem to confirm that the apparent displacement effect on belugas extends farther than has been shown for other small odontocetes exposed to airgun pulses (e.g., Harris et al. 2007). Similarly, aerial survey results from another seismic (array specifications unknown) and exploratory drilling activity conducted in the same area and same season in 2007 to 2008 showed belugas widely distributed offshore during the operation period, yet rarely sighted from seismic ships. This was interpreted as a tendency to temporarily avoid areas of seismic activity by greater distances than the range covered by MMOs on board seismic vessels (Harwood et al. 2010). However, the authors highlighted the temporary nature of these displacements, as belugas were observed back in the seismic operation area within days after the end of the seismic operations. Belugas have been shown to have greater displacement in response to a moving sound source (e.g., air gun activity on a moving vessel) and less displacement or behavioral change in response to a stationary sound source. The presence of belugas has been documented within ensonified zones of industrial sites near platforms and stationary dredges, and the belugas did not seem to be disturbed by the activity (Richardson et al. 1995).


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9. Literature Cited in Report Angliss, R.P., and Lodge, K.L. (2002). Alaska marine mammal stock assessments, 2002. U.S. Department of Commerce, NOAA Tech. Memo. NM FSAFSC-133, 224 p.

Aspinall, W. (2010). A route to more tractable expert advice. Nature, 463, 20–21.

Barber, J.R., Crooks, K.R., and Fristrup, K.M. (2010). The costs of chronic noise exposure for terrestrial organisms. Trends in Ecology and Evolution, 25 (3), 180-1.

Beale, C.M., and Monaghan, P. (2004) Human disturbance: people as predation-free predators? Journal of Applied Ecology, 41, 335–343.

Blumstein, D.T., Fernández-Juricic, E., Zollner, P.A., and Garity, S.C. (2005). Inter-specific variation in avian responses to human disturbance. Journal of Applied Ecology, 42, 943–953.

Booth, C.G., Donovan, C., Plunkett, R., and Harwood, J. (2016). Using an interim PCoD protocol to assess the effects of disturbance associated with US Navy exercises on marine mammal populations: Final Report. Report code SMRUC-ONR-2016-004, submitted to the Office of Naval Research – Marine Mammal and Biology Program, February 2016 (unpublished).

Burgman, M. A., McBride, M., Ashton, R., Speirs-Bridge, A., Flander, L., Wintle, B., Fidler, F., Rumpff L., and Twardy C. (2011). Expert status and performance. PloS one, 6(7), e22998. doi:10.1371/journal.pone.0022998.

Cairns, D.K. 1988. Seabirds as indicators of marine food supplies. Biological Oceanography 5: 261-271.

Chang, W., Cheng, J., Allaire, J.J., Xie, Y., and McPherson, J. (2015). shiny: Web Application Framework for R. R package version 0.12.2. http://CRAN.R-project.org/package=shiny Cornick, L.A., Quakenbush, L.A., Norman, S.A., Pasi, C., Maslyk, P., Burek, K.A., Goertz, C.E.C., and Hobbs, R.C. (2016). Seasonal and developmental differences in blubber stores of beluga whales in Bristol Bay, Alaska using high-resolution ultrasound. Journal of Mammalogy http://dx.doi.org/10.1093/jmammal/gyw074.

Delbecq, A., Van de Ven, A., and Gustafson, D. (1975). Group techniques for program planning: a guide to nominal group and Delphi processes. Glenview, IL: Scott, Foresman and Company.

Frid, A. and Dill, L. (2002) Human-caused disturbance stimuli as a form of predation risk. Conservation Ecology, 6, 11.

Harwood, J., and Booth, C.G. (2016). The application of an interim PCoD (PCoD Lite) protocol and its extension to other marine mammal populations and sites. Final Report. Report Code SMRUC-ONR-2016-004, Submitted to The Office of Naval Research – Marine Mammal and Biology Program, February 2016 (Unpublished).

Harwood, A. J., King, S., Schick, R.S., and Donovan, C. (2014). A Draft Protocol for Implementing the Interim Population Consequences of Disturbance (PCOD) Approach: Assessing the Effects of UK Offshore Renewable Energy Developments on Marine Mammal Populations. SMRU Marine Report to the Crown Estate SMRUL--TCE-2013-014. Scottish Marine and Freshwater Science, 5 (2) (http://www.scotland.gov.uk/Resource/0044/00443360.pdf).

Heinis, F., de Jong, C.A.F.,and Rijkswaterstaat Underwater Sound Working Group (2015) Cumulative effects of impulsive underwater sound on marine mammals. TNO report R10335-A. https://www.noordzeeloket.nl/en/Images/Framework%20for%20assessing%20ecological%20and%20cumulative%20effects%20of%20offshore%20wind%20farms%20-

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Hobbs, R.C., Rugh, D.J., and DeMaster, D.P. (2000). Abundance of belugas, Delphinapterus leucas, in Cook Inlet, Alaska, 1994-2000. Marine Fisheries Review, 62, 37-45.

Hobbs, R.C., Shelden, K.E.W., Rugh, D.J., and Norman, S.A. (2008). 2008 status review and extinction risk assessment of Cook Inlet belugas (Delphinapterus leucas). AFSC Processed Rep. 2008-02, 116 p. Alaska Fish. Sci. Cent., NOAA, Natl. Mar. Fish. Serv., 7600 Sand Point Way NE, Seattle WA 98115.

Hobbs, R.C., Wade, P.R., and Shelden, K.E.W. (2016). Viability of a small, geographically-isolated population of beluga whales, Delphinapterus leucas: Effects of hunting, predation, and mortality events in Cook Inlet, Alaska. Marine Fisheries Review 77 (2), 59-88. doi: 10.7755/MFR.77.2.4

Holt, M.M, Noren, D.P., Dunkin, R.C., and Williams, T.M. (2015). Vocal performance affects metabolic rate in dolphins: implications for animals communicating in noisy environments. The Journal of Experimental Biology, 218, 1647-1654.

Kahneman, D. (2012). Thinking, Fast and Slow. Penguin.

Kastelein, R.A., Ford, J., Berghout, E., Wiepkema, P.R., and van Boxsel, M. (1994). Food consumption, growth and reproduction of Belugas (Delphinapterus leucas) in human care. Aquatic Mammals, 20 (2), 81-97.

King, S., Schick, R.S., Donovan, C., Booth, C.G, Burgman, M., Thomas, L., and Harwood, J. (2015). An Interim Framework for Assessing the Population Consequences of Disturbance. Methods in Ecology and Evolution. doi: 10.1111/2041-210X.12411

Martin, T., Burgman, M., Fidler, F., Kuhnert, P., Low-Choy, S., McBride, M. and Mengersen, K. (2012). Eliciting Expert Knowledge in Conservation Science. Conservation Biology, 26, 29–38.

Nabe-Nielsen J., Sibly, R.M., and Tougaard, J. (2014). Effects of noise and by-catch on a Danish harbour porpoise population. Ecological Modelling, 272, 242–51.

National Research Council. (2005). Marine Mammal Populations and Ocean Noise: Determining when noise causes biologically significant effects. The National Academy Press. Washington D.C.

New, L.F., Moretti, D.J., Hooker, S.K., Costa, D.P., and Simmons, S.E. (2013). Using energetic models to investigate the survival and reproduction of beaked whales (family Ziphiidae). PloS One, 8 (7), e68725. doi: 10.1371/journal.pone.0068725

New, L., Clark, J., Costa, D., Fleishman, E., Hindell, M., Klanjšček, T., Lusseau, D., Kraus, S., McMahon, C.R., Robinson, P.W., Schick, R.S., Schwarz, L.K., Simmons, S.E., Thomas, L., Tyack, P.L., and Harwood, J. (2014). Using short-term measures of behaviour to estimate long-term fitness of southern elephant seals. Marine Ecology Progress Series, 496, 99–108.

NMFS. (2015). Draft Recovery Plan for the Cook Inlet Beluga Whale (Delphinapterus leucas). National Marine Fisheries Service, Alaska Regional Office, Protected Resources Division, Juneau, AK.

Norman, S.A. (2011). Nonlethal anthropogenic and environmental stressors in Cook Inlet beluga whales (Delphinapterus leucas). Report prepared for NOAA Fisheries, National Marine Fisheries Service, Anchorage, Alaska. NMFS contract no. HA133F-10-SE-3639. 113 p.

O'Hagan, A., Buck, C.E., Daneshkhah, A., Eiser, J.R., Garthwaite, P.H., Jenkinson, D.J., Oakley, J.E., and Rakow. T. (2006) Uncertain Judgements: Eliciting Experts' Probabilities. Wiley. ISBN: 978-0-470-02999-2.




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Rugh, D.J., Shelden, K.E.W., and Hobbs, R.C. (2010). Range contraction in a beluga whale population. Endangered Species Research, 12, 69-75.

Runge, M., Converse, S., and Lyons, J.E. (2011). Which uncertainty? Using expert elicitation and expected value of information to design an adaptive program. Biological Conservation, 144, 1214–1223.

Scheifele, P.M., Andrew, S., Cooper, R.A., Darre, M., Musiek, F.E., and Max, L. (2005). Indication of a Lombard vocal response in the St. Lawrence River beluga. Journal of the Acoustical Society of America, 117 (3), 1486-1492.

Shelden, K.E.W., Sims, C.L., Vate Brattstrӧm, L., Goetz, K.T., and Hobbs, R.C. (2015). Aerial surveys of beluga whales (Delphinapterus leucas) in Cook Inlet, Alaska, June 2014. AFSC Processed Rep. 2015-03, 55 p. Alaska Fish. Sci. Cent., NOAA, Natl. Mar. Fish. Serv., 7600 Sand Point Way NE, Seattle, WA 98115.

Small, B., Castellote, M., Lammers, M., Jenniges, J., Rosinski, A., Atkinson, S., Garner, C., and Moore, S. (2011). Seasonal distribution of Cook Inlet beluga whales based on passive acoustic monitoring. Presented at the 2011 Alaska Marine Science Symposium, Anchorage, AK.

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Sutherland, W.J., Armstrong-Brown, S., Armsworth, P.R., Tom, B., Brickland, J., Campbell, C.D., and Watkinson, A.R. (2006). The identification of 100 ecological questions of high policy relevance in the UK. Journal of Applied Ecology, 43, 617–627.

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10. Glossary of Terms

Acute effect The direct effect of a change in behaviour or physiology on vital rates

Body condition A measure of an individual's energy stores. In marine mammals, usually blubber thickness or total body lipid. One component of health (q.v.)

Chronic effect The indirect effect of a change in behaviour or physiology on vital rates (q.v.) via individual health (q.v.)

Demographic rates The average survival and fecundity rates, and ages at independence and first breeding experienced by all members of a population in a particular year

Demographic stochasticity

Variation among individuals in their realised vital rates (q.v.) as a result of random processes

Environmental variation

Variation in demographic rates (q.v.) among years as a result of changes in environmental conditions

Expert elicitation A formal technique for combining the opinions of many experts. Used in situations where there is a relative lack of data but an urgent need for conservation decisions

Fecundity The average of individual fertility rates for all members of a population

Fertility The probability that an individual adult female will give birth to a viable offspring in any particular year

Fitness A relative term reflecting the potential contribution of the genotype of an individual to future generations. The fittest individuals leave the greatest number of descendants relative to the number of descendants left by other individuals in the population

Health All internal factors that may affect individual fitness (q.v.) and homeostasis, such as body condition (q.v.), and nutritional, metabolic, and immunological status

Uncertainty Incomplete information about a particular subject. In this report, we are only concerned with those components of uncertainty that can be quantified

Vital rates

The probability that an individual will survive from one year to the next, the probability that an individual adult female will give birth in one year

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