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TECHNICAL APPENDIX

VOLUME B - OFFSHORE CHAPTER 11 - MARINE MAMMALS APPENDIX 11.3 - STATIC ACOUSTIC MONITORING FOR

HARBOUR PORPOISES AND DOLPHINS IN AND AROUND THE PROPOSED NAVITUS BAY OFFSHORE WIND PARK USING C-PODS

DOCUMENT 6.2.2.11.3

APRIL 2014

Navitus Bay Wind Park

Environmental Statement

This document has been prepared to provide information in respect of the proposed Navitus

Bay Wind Park and for no other purpose.

In preparation of this document Navitus Bay Development Limited and their subcontractors

have made reasonable efforts to ensure that the content is accurate, up to date and complete

for the purpose for which it has been prepared.

Other than any liability detailed in the contracts between the parties for this work. neither

Navitus Bay Development Limited or their subcontractors shall have any liability for any loss,

damage, injury, claim, expense, cost or other consequence arising as a result of use or

reliance upon any information contained in or omitted from this document.

© Copyright Navitus Bay Development Limited 2014

Technical Appendix

STATIC ACOUSTIC MONITORING FOR

HARBOUR PORPOISES AND DOLPHINS IN

AND AROUND THE PROPOSED NAVITUS

BAY OFFSHORE WIND PARK USING C-PODS

DR. CORMAC BOOTH & CLAIRE LACEY

Date: 10th March 2014

Report Code: SMRUL-ENO-2013-015

This report is to be cited as: Booth, C.G. & Lacey, C. (2014). “Static acoustic monitoring for harbour

porpoise and dolphins in and around the proposed Navitus Bay offshore wind park using C-PODs.”

SMRU Ltd report number (SMRUL-ENO-2013-015). March, 2014 (unpublished).

Approved by: Carol Sparling

CONTENTS

1 Summary.................................................................................................................................................... 3

2 Introduction ............................................................................................................................................... 4

2.1 Site Location .......................................................................................................................................... 4

2.2 Passive Acoustic Monitoring & C-PODs ................................................................................................. 5

3 Methods .................................................................................................................................................... 7

3.1 Deployment Details ............................................................................................................................... 7

3.2 Click detection ..................................................................................................................................... 10

3.3 Analysis – Dolphin data ....................................................................................................................... 10

3.4 Analysis – Porpoise data ..................................................................................................................... 10

3.4.1 Candidate covariates ...................................................................................................................... 11

3.4.2 Survey covariates ............................................................................................................................ 12

3.4.3 Biological/Geographic covariates ................................................................................................... 12

3.4.4 Investigating collinearity ................................................................................................................. 13

3.4.5 Model selection .............................................................................................................................. 13

4 Results ..................................................................................................................................................... 14

4.1 Detection rates .................................................................................................................................... 14

4.1.1 Dolphins .......................................................................................................................................... 14

4.1.2 Harbour porpoise ............................................................................................................................ 15

4.1.2.1 Fitted relationships and the relative importance of each covariate ........................................... 16

4.1.2.2 The (minor) effect of Latitude ..................................................................................................... 17

4.2 C-POD Performance ............................................................................................................................ 18

4.2.1 Effect of noisy environment ........................................................................................................... 18

5 Discussion ................................................................................................................................................ 19

Click Max ....................................................................................................................................................... 20

Time of year .................................................................................................................................................. 21

POD ID........................................................................................................................................................... 21

Distance from land ....................................................................................................................................... 22

6 References ............................................................................................................................................... 22

1 SUMMARY

This report details the findings of a programme of passive acoustic monitoring on and around the

Navitus Bay Offshore Wind Park development site off the south coast of Dorset and Hampshire. C-

PODs were deployed to detect dolphins and porpoises. Two separate C-POD deployments were

carried out on the site. The first consisted of 4 units deployed in a line across the proposed

development area between November 2011 and July 2012. A second array consisting of 6 C-POD

units was installed in September 2012 to the north-west of the site, to assess the usage by dolphins

of the area between the development site and the coast. This was considered to be particularly

important due to concerns surrounding the potential for the noise generated during wind park

construction to impact upon the bottlenose dolphins which are regularly sighted along the Swanage

and Purbeck coast, coupled with uncertainty over how far offshore these dolphins range. From the

first deployment C-PODs were only recovered at three of the four sites, while only four of the six C-

PODs deployed in the 2nd locations were recovered. Porpoise activity across the sites was low with

an animal detected on approximately 1/3 of monitoring days (averaged across sites) and detection

rates within each day were generally infrequent. Dolphin detection rates were extremely low as

there were only a total of 6 dolphin detections across all the sites over the entire monitoring period.

Modelling of the C-POD data using General Additive Modelling (GAMs) and Generalised Estimating

Equations (GEEs) demonstrated that several factors influenced porpoise detections. These were:

how many clicks were logged in each monitoring minute (the ‘ClickMaxbuffer’), the time of year (i.e.

Julian day) and the POD unit that was deployed. Porpoise detection rates decreased significantly as

the number of clicks logged in each minute increased. The ClickMax buffer is an indication of the

level of background noise and therefore the observed pattern suggests that porpoise detections

decreased as background noise increased. This could be explained by animals avoiding periods/areas

of high noise, or the C-PODs performance being affected by the conditions. Porpoise detection rates

varied significantly throughout the year, peaking between December and March and lowest

between July - October. A weak (i.e. not statistically significant) pattern was observed with porpoise

detections increasing as latitude decreased (i.e. sites further from land having higher detection

rates). This monitoring indicates that porpoise and dolphin detection rates are very low in the region

around the Navitus Bay wind park.

2 INTRODUCTION

2.1 SITE LOCATION

As described in Volume B, Chapter 4, (Offshore Alternatives), feedback received during the

consultation process with both the public and other interested stakeholders, along with assessments

undertaken as part of the EIA process, informed changes to the boundary of the Offshore

Development Area. As a result, the Turbine Area described and assessed within the Preliminary

Environmental Information and in this Technical Appendix ('the PEI3 Turbine Area'; shown in Figure

1) differs from that presented within the Environmental Statement (the 'Application Turbine

Area'). The boundary changes to the Turbine Area resulted in a reduction in the area of 22 km2 and

consequently a reduction in the maximum number of Wind Turbine Generators (WTG) from 218

(PEI3) to 194 (Application). No changes have been made to the density of development within the

revised boundary or to the turbine envelopes or the foundation parameters and options. However,

the distances between the turbine area and some of the coastal receptors have increased.

Baseline surveys and modelling predate the decision to reduce the size of the Turbine Area and

consequently this Technical Appendix provides information on the acoustic monitoring designed in

light of the Offshore Development Area PEI3 boundary, within which the entirety of the revised

Turbine Area lies. No changes have been made to the Turbine Area boundary or to the description

of the Project that could lead to impacts on areas not assessed during PEI3, or to effects of a

different nature to those assessed during PEI3. Therefore, the survey data collected, as contained in

this Technical Appendix, provides the information required to identify and assess the likely

significant environmental effects of the Project within the Offshore Development Area for which

development consent is now sought.

The Navitus Bay site is located approximately 14 km from the closest point to the coast (Figure 1).

Figure 1 – Location of the Navitus Bay Round 3 offshore wind park off the south coast of the UK.

2.2 PASSIVE ACOUSTIC MONITORING & C-PODS

In order to inform the Environmental Impact Assessment (EIA), there is a requirement to determine

the relative importance of the location to marine mammal species such as dolphins and harbour

porpoises protected under European Law. One way of monitoring an area to determine the relative

importance of an area over an extended temporal period is to use Passive Acoustic Monitoring

(PAM).

Typically, cetacean surveys have used visual methods to detect animals; but small cetaceans such as

harbour porpoises and other delphinids (i.e. dolphins) can be extremely difficult to observe in the

wild as they spend the majority of their time underwater, generally occur in small groups, and often

present a low profile at the surface. This can result in an underestimation of animal presence. These

species are much easier to detect acoustically, as they regularly produce sounds for orientation,

navigation, foraging and communication. Passive Acoustic Monitoring (PAM) is the term used to

describe the process of passively listening for sounds emitted by animals. This is a widely used tool

that allows the study of submerged animals that are not visible to human observers (many species of

marine mammal spend 90% of their time submerged) and, if properly implemented, it does not

interfere with the animals’ behaviour (Zimmer, 2011). Harbour porpoise sightings rates, in particular,

are heavily impacted by poor sea conditions, with rates dropping rapidly beyond sea state 2

(Hammond et al., 2002). Beyond sea state 4 it becomes very difficult to observe many other

cetaceans. Fortunately, many cetaceans can be reliably detected using passive acoustic methods and

the technique allows for data collection to continue in much higher sea states and at night. It should

be noted, however, that only vocalising animals will be detected.

A PAM survey was conducted on and around the Navitus Bay site using C-PODs between November

2011 and January 2013 across 10 sites. The C-POD, a PAM product manufactured by Chelonia Ltd., is

a self-contained submersible ultrasound monitoring units that include a hydrophone element, an

amplifier, analogue electronic filters and a digital processor, as well as a battery pack and memory.

They do not record sound but work by logging the start and end of echolocation clicks of porpoises

and dolphins to a 5 microsecond resolution. Cetacean clicks are generally of around 50-400 µs

duration. Clicks are detected by comparing the narrow band of frequencies within the 20 – 160 kHz

frequency range and outputs outside of that range. The basic metric they generate is Detection

Positive Minutes (DPM) which is defined as any minute in which a cetacean click train is detected.

Harbour porpoises are the only species of porpoise likely to be present at the site therefore any

porpoise clicks detected will be harbour porpoise.

C-PODs or ‘PODs’ have been used in a number of studies to assess the distribution of harbour

porpoises in the context of monitoring marine renewable energy developments and interactions

with static fishing nets and “pingers”, which are acoustic devices used to reduce harbour porpoise

bycatch (e.g. Booth, et al. 2011; Teilmann et al. 2005; Todd, et al. 2009).

PODs log continuously 24 hours a day and are therefore useful for providing continuous data on

porpoise and dolphin activity within a radius of a few hundred metres. However it is important to

clarify their limitations:

They provide data on porpoise and dolphin acoustic activity (acoustic absence/presence) in a given geographical area (i.e. within a certain range of the hydrophone).

They can be used to compare relative frequency of occurrence/echolocation activity between sites or through time.

They will only record porpoises and dolphins that are actively echolocating.

They cannot, on their own, be used for estimating abundance of porpoises / dolphins.

They cannot distinguish porpoise signals from other narrow-band high frequency sounds.

They do not identify dolphins to a species level.

As it is not possible to determine a count of cetaceans from POD records (e.g. a period of 10 DPM

may indicate multiple porpoises passing and echolocating on a POD, or it may be a single animal

echolocating in proximity to a POD for 10 minutes), here we investigate patterns of ‘cetacean

activity’ which encapsulates either increased cetacean occurrence or increased cetacean

echolocation (or a combination of the two). PODs will only record animals that are actively

echolocating as they pass, and then only those whose orientation is in the field where the POD can

pick it up (i.e. animals that echolocate towards the POD). Harbour porpoise clicks are detectable

within an approximate 250m radius of the unit, and it is generally assumed, from captive studies,

that porpoises echolocate more or less continuously while foraging or travelling (Teilmann et al,

2005, Verfuß et al, 2007). However there is evidence that animals may increase echolocation rates

during the night (Akamatsu, et al. 1994; Carlström, 2005). C-PODs are thought to detect dolphins out

to a range of 1 km.

Gardline Environmental Ltd (GEL) were commissioned by Titan Environmental Surveys Ltd (TESL) on

behalf of Navitus Bay Ltd at the Navitus Bay Wind Park to provide marine mammal monitoring

surveys using C-PODS deployed at four locations across the site for a total of 12 months (Gruden and

Riddoch 2012). Following this initial period of monitoring, an additional 6 monitoring sites were

chosen for a three month deployment to the west of the offshore wind farm site to characterise in

particular the use of the area between the wind farm site and the coast by bottlenose dolphins. The

objective of these deployments of C-PODs was to determine the occurrence of porpoises and

dolphins to inform the Environmental Impact Assessment. The following report presents the results

of the SMRU Ltd. analyses of the data collected by GEL on behalf of TESL.

3 METHODS

3.1 DEPLOYMENT DETAILS

Initially, C-PODs were deployed at four locations, labelled A - D across the development area in a

north-south running line between November 2011 and July 2012. Details of the deployment of the

units can be found in Gruden and Riddoch (2012). Later, C-PODs were deployed in a new array to the

north-west of the development site (labelled aA – aF) between September 2012 and January 2013

(Figure 2). C-PODs were rotated between deployment sites. Initial deployment was conducted in

November 2011 and four data retrievals have been conducted to date. Data collection, once

commenced, continued unbroken at sites B and C. Site A is missing data from the initial deployment,

and site D only has data from deployment 2 (Table 1) due to loss of units.

Figure 2 – Location of C-POD deployments within the Navitus Bay Round 3 offshore wind park footprint.

Table 1 – Summary details of C-POD deployments.

Site Latitude Longitude Depth

(m) PAM Start PAM End Notes

A 50.5533 -1.7191 30 31/01/2012 23/07/2012 Unit deployed on 10/11/2011 but lost

B 50.5165 -1.7362 34 08/11/2011 23/07/2012

C 50.4737 -1.7637 35 08/11/2011 23/07/2012

D* 50.4337 -1.8099 33 31/01/2012 26/02/2012 Unit deployed on 08/11/2011 but lost

aA 50.4700 -1.8956 38 15/09/2012 -- No data - unit deployed on 15/09/2012 but lost

aB 50.5161 -1.8381 37 15/09/2012 06/01/2013

aC 50.5057 -1.9556 34 15/09/2012 -- No data - unit deployed on 15/09/2012 but lost

aD 50.5447 -1.8871 34 16/09/2012 09/01/2013

aE 50.5522 -2.0043 30 16/09/2012 09/01/2013

aF 50.5825 -1.9370 27 16/09/2012 07/01/2013

*C-POD found washed up on shore. Examination of the data suggests that the C-POD broke from its mooring on

26/02/2012.

Figure 3 - Summary of deployment details showing the spread of data collection across sites and the monitoring period. Single point

indicated unit was deployed but no useable data were retrieved.

PODs were deployed on moorings comprising an L-shaped configuration with two anchor points

(Figure 4). The PODs were fixed to the riser from the small anchor point to facilitate the data

download and servicing of PODS during visits. The POD mooring is designed to keep the unit sitting

upright in the water column. When data from the PODs are downloaded, the proprietary software

also provides information on the tilt angle of the PODs at regular intervals through the period of

deployment.

Figure 4 - Details of mooring upon which C-POD was deployed.

3.2 CLICK DETECTION

Initial data processing was performed using C-POD.exe, which allows visual analysis of all logged

clicks and an assessment of how these clicks have been classified into different click trains (using the

KERNO classifier). The C-POD software package will automatically classify any click trains into one of

five categories, reflecting how likely the train is to be a porpoise or dolphin click train. These are “Hi”

(high quality), “Mod” (moderate quality), “Lo” (low quality), “?” (unknown), and “all Q” (all quality).

This allows the user to check for incorrect assignment of click-trains. Boat sonars can be confused

with porpoise click trains for example, and likewise some trains incorrectly classified as being from

boat sonar could be reclassified as cetacean echolocation clicks.

C-POD software generates warnings on data quality when NBHF (Narrow Band High Frequency – in

this region highly likely to be harbour porpoise) or dolphin clicks rates are low. These warnings

indicate whether NBHF or dolphin clicks need to be verified by a human observer. Only Hi and Mod

quality detections have been used in the following analyses. The use of the highest quality click train

type may mean that occasional true click trains that are detected by the C-PODs have been rejected,

but it minimises the inclusion of false positive detections in data analysis which should be the

priority in assessing cetacean activity in proximity to the Navitus Bay site.

3.3 ANALYSIS – DOLPHIN DATA

Click data were run through the KERNO classifier as described above. Due to the low number of

detections classified as dolphins, and importance of understanding dolphin distribution at the

Navitus Bay site, all of the dolphin detections reported by the KERNO detection algorithm were

verified by hand, and sent to Chelonia Ltd. for secondary verification. There were insufficient

detections to conduct any further statistical analyses on the dolphin data.

3.4 ANALYSIS – PORPOISE DATA

For analysis purposes, the C-POD outputs were summarised into hour-long listening intervals in

which porpoises were detected. The response variable used in the modelling was a binary value

indicating whether one or more porpoise detection positive minutes occurred in the hour (DPH) (1)

or not (0). The response was modelled with respect to a range of covariates (Table 3) inside a

Generalised Additive Model (GAM) with logit link and binomial errors. GAMs have been extremely

useful in modelling marine mammal habitat preferences and distribution patterns (Cañadas and

Hammond 2008; DeSegura, et al. 2008; Marubini et al. 2009; Skov and Thomsen 2008). However,

one of the assumptions of GAM methods is that the model errors are independent. This is unlikely to

be the case with a C-POD dataset as observations were collected close together in time (and/or

space). Therefore, this auto-correlation must be accounted for in the modelling approach, in order

for realistic conclusions to be drawn.

Generalised Estimating Equations (GEEs) are an extension of Generalised Linear Models (GLMs),

facilitating regression analyses longitudinal data and non-normally distributed variables (Liang &

Zeger 1986; Hardin & Hilbe, 2002). GEEs can be used to account for temporal and spatial auto-

correlation within a dataset as they function by replacing the assumption of independence with a

correlation structure. Data within the model are grouped into a series of ‘panels’, within which

model errors are allowed to be correlated and between which data are assumed to be independent.

A suitable ‘panel’ size was chosen using autocorrelation function plots and a simple working

independence correlation model structure was also selected. This model structure provides identical

coefficients to those of a standard GAM-based approach, but the standard errors will differ

significantly under the GEE structure, strongly influencing final model selection results, avoiding the

incorrect inclusion of covariates. GEEs have also been used in other circumstances to estimate

cetacean habitat preferences from auto-correlated data (Booth, et al. 2013; Panigada et al. 2008).

In this analysis, a GAM built within a GEE model construct was used to explain harbour porpoise

habitat preferences across the study site. Because we used a binomial model (with values of 0 or 1)

and there were a large proportion of zeroes in the raw data, it is likely that there will be a very low

mean-variance relationship. This may result in underestimation of the uncertainty around model

estimates. This was a function of the low detection rates of porpoises and was difficult to deal with

in the modelling.

All statistical analyses were conducted using the computer package ‘R’ (R Core Development Team,

2006 - available at http://www.r-project.org/). Within R a series of statistical packages (or programs)

are utilised to perform bespoke analyses. The following ‘packages’ were used here to assess

porpoise activity in the Navitus Bay region:

mgcv (used to fit Generalised Additive Models to the data)

geepack (used to fit Generalised Estimating Equations to the data)

splines (used to control the fit of smooth relationships to data)

car (to investigate collinearity (ie. pre-existing patterns) in covariate and porpoise detection

data

3.4.1 CANDIDATE COVARIATES

A range of environmental, oceanographic and survey (i.e. impacting detection) variables are known

to impact studies of cetacean habitat preference (Cañadas and Hammond 2008; DeSegura, et al.

2008; Marubini et al. 2009; Skov and Thomsen 2008). Consequently a range of candidate covariates

were considered for the models. A summary of these factors is shown below (Table 2).

Table 2 - Candidate covariates used in modelling POD data.

Candidate Covariate Description

Noise (ClickMax) The number of clicks logged by the C-POD in any minute (max = 4096)

Angle The tilt angle of the C-PODs

Time of year The Julian Day of the year in which data was collected

DayNightIndex Position in Day/Night (0/2 = sunrise, 0.5 = middle of day, 1 = sunset, 1.5 = middle of night)

ClosetoSW Position in the spring-neaps tidal cycle (0/1 = spring tides, 0.5 = neap tides)

TimeFromLW (TFLW) Position in the daily tidal cycle (0/1 = low water, 0.5 = high water)

Latitude/Longitude Spatial coordinates of C-POD deployment locations

Temperature Water temperature (°C) logged by the C-POD.

3.4.2 SURVEY COVARIATES

To investigate the potential for differing detection sensitivities between C-POD units, C-POD ID was

included as a covariate in the models (as PODs were rotated between sites in the first site). In

addition, the site at which C-PODs were deployed was included to capture spatial differences in the

study region. When configuring C-PODs prior to deployment it is possible to set the maximum

number of clicks to be logged (henceforth referred to as ‘ClickMax’) in any given minute of

monitoring. Selecting the ‘ClickMax’ is a trade-off between maximising the amount of monitoring

time in each minute (it is possible for the ClickMax to be reached in <1minute in noisy environments)

against running out of storage space before the C-PODs are retrieved (and losing valuable

monitoring time). Here the default value of 4096 clicks per minute was selected as the ClickMax. As a

proxy for background noise, the ClickMax averaged over each monitoring hour was used as a

covariate to determine whether either porpoises were avoiding periods or areas of high noise

and/or detection of porpoise click trains was impacted by periods of high noise.

3.4.3 BIOLOGICAL/GEOGRAPHIC COVARIATES

Seasonal variations in harbour porpoise habitat use have been observed in a number of studies using

C-PODs and acoustic surveys (SMRU Ltd, 2011; Verfuß, et al. 2007). Consequently, in this report

‘Month’ was included as a candidate covariate to determine if harbour porpoises exhibited seasonal

usage patterns in the region.

Previous studies have investigated whether harbour porpoises have diurnal patterns of movement

and/or vocalisation behaviour (Akamatsu, et al 1994, Carlström, 2005, Todd et al. 2009). To assess

whether harbour porpoise exhibit such shifts in the study region, ‘DayNightIndex’ was included in

the models as a candidate covariate - as continuous indices between 0 and 2, values between 0

(sunrise) and 1 (sunset) indicating day-time and values between 1 (sunset) and 2 (following days

sunrise) indicating night-time.

Porpoise distribution has been associated with state of tide, with animals appearing more prevalent

or more detectable during certain phases of tide (e.g. slack, flood, ebb). A range of studies of their

distribution have identified site-specific patterns associated with tidal activity (Calderan 2003;

Embling et al. 2010; Johnston et al. 2005; Pierpoint 2008; Skov and Thomsen 2008). Here,

‘ClosetoSW’ (position in the lunar tidal cycle, i.e. spring-neaps cycle) and ‘Time from Low Water’

(TFLW) (position in the daily tidal cycle) were used to determine whether the tidal activity in the

Navitus Bay region was influencing porpoise occurrence and/or acoustic activity. These tidal

covariates were created using tidal port data from Bournemouth (in POLTIPS v.3).

3.4.4 INVESTIGATING COLLINEARITY

Collinearity between covariates, if unaccounted for in models, can cause inflated or underestimated

standard errors and p-values and lead to poor model selection. To avoid this, collinearity between

predictor variables was investigated prior to modelling using ‘variance inflation factors’ (VIF) (Cox

and Snell 1989; Fox and Monette 1992) using the vif function in the ‘car package’ in R. Large VIF

values indicate collinearity and a threshold of VIF = 10 was used here. VIFs > 10 resulted in the

retention of the covariate with the best fit to the data, and the other covariates being removed

(Table 3).

Table 3 - Details of assessing collinearity between covariates and those retained.

Candidate Covariate Collinearity Issues? Retained?

Noise (ClickMax) N --

DayNightIndex N --

ClosetoSW with TimeFromLW No (TimeFromLW retained)

TimeFromLW with ClosetoSW Yes

Temperature With Month No (Month retained)

Angle N --

Latitude/Longitude N --

Time of year with Temperature Yes

3.4.5 MODEL SELECTION

A single main model was constructed using all the porpoise data collected from the study site

combined. Candidate covariates were offered in the model selection phase to construct the final

models. Covariates were selected for models using a manual stepwise selection using QIC.

Specifically, a global (all terms) model was constructed and QIC was used to govern model selection

of all possible variations of that model. Following this, GEE-based p-values were used to determine

the statistical significance of each covariate and terms with large p-values were removed from the

model. The relative importance of each covariate was assessed by looking at the drop in QIC caused

by removing a covariate from the full model.

4 RESULTS

4.1 DETECTION RATES

4.1.1 DOLPHINS

Six verified dolphin detections were made during the duration of this study across the ten C-POD

sites (Table 4 & Figure 5).

There are too few data points to conduct any further analyses on these data, and unfortunately it is

not possible to identify species from these detections. However it is noteworthy is that dolphin

detections were very infrequent (2 in April, 1 in July, 1 each in October, November, December), very

short (only one lasted over 10 minutes) and only occurred on three sites (2 at site C, 1 at B and 3 at

aD). Because of the low detection rates for dolphins, it was not possible to statistically analyse these

data using the method by which the porpoise data were analysed.

Table 4 - Times and locations of dolphin detections made during the deployments at Navitus Bay. Only six detections were found to be

dolphins following verification (three in the original sites (A-D) and three in the additional deployment).

Site Deployment Start time End Time

C 3 07/04/2012 22:02 07/04/2012 22:03 C 3 08/04/2012 00:02 08/04/2012 00:09 B 4 16/07/2012 22:04 16/07/2012 22:16

aD -- 18/10/2012 12:23 18/10/2012 12:30 aD -- 03/11/2012 06:53 03/11/2012 06:53 aD -- 25/12/2012 22:05 25/12/2012 22:05

Figure 5 – C-POD locations with dolphin detections (cyan circles) from the monitoring program.

4.1.2 HARBOUR PORPOISE

Porpoises were detected on 31% of days during the study period. For each site the number of hours

with ≥1 detection positive minute(s) in each C-POD dataset (DPMpH) and the percentage of the

dataset with detection positive minutes (% of DPH) was calculated (Table 5). At all sites the detection

rates for porpoises were generally low ranging between 0.9-3.6 % of hours with DPM and a mean of

0.021-0.086 DPMpH.

Within each of the regions there were variations in porpoise activity between sites. Detections rates

were lowest at sites A (Nov 2011 – July 2012) and aF (Sept 2012 – Jan 2013). Detection rates were

highest at the most offshore sites (C and aB) with porpoises present between 2.5-3.6% of the

monitoring period.

It is clear from the very low mean detection rates throughout the study, that there are a large

number of zeroes in the data. This means that the potential variance in the models will be small,

which may result in underestimation of confidence limits around the estimates (i.e. fitted

relationships may look more certain than they are in reality).

Table 5 - Summary harbour porpoise detection data for the Navitus Bay C-POD deployments

Site Detections No. Hours #Days % of DPH MeanDPMpH %DPD

A 59 4027 168 1.465 0.025 (0.24) 26.8

B 109 6072 253 1.795 0.041 (0.42) 28.5

C 168 4630 193 3.629 0.086 (0.63) 51.8

D -- -- -- -- -- --

aB 67 2711 113 2.471 0.055 (0.46) 36.3

aD* 14 696 27 2.011 0.029 (0.22) 33.3

aE 32 2016 84 1.587 0.037 (0.37) 28.6

aF 24 2424 101 0.990 0.021 (0.24) 16.8

* - data from site aD had to be deleted due to issues with the data collection (see section 4.1).

4.1.2.1 FITTED RELATIONSHIPS AND THE RELATIVE IMPORTANCE OF EACH COVARIATE

The relationship between predictor variables and the response are shown below (Figure 6). In each

plot, the horizontal x-axis is the variable we are interested in investigating the change in porpoise

activity with, e.g. time of year. The vertical y-axis explains how porpoise acoustic activity changes as

the variable of interest (x-axis) changes. The associated dotted lines show the 95% confidence

intervals for the predicted relationships (i.e. it is highly likely that the ‘true’ relationship fits between

the upper and lower dotted lines). Only predictor variables that had a significant influence on

porpoise activity and that were retained in the model are shown – therefore if no plot is shown for a

covariate discussed above, it means that porpoise acoustic activity did not change with respect to

this variable (see section . Once the final structure for the model was determined, the relative

importance of each covariate was investigated using the loss in QIC (a measure giving an estimate of

how well a covariate explains the variability in the data) when each covariate was omitted from the

final models (Table 6).

In the model the following covariates were significant in explaining the variation in detections

(relationships also shown in Figure 6):

ClickMax – Porpoise detection rates decreased significantly as the number of clicks logged in each

minute increased. This pattern could be explained by porpoises avoiding the region during ‘noisy’

periods. It is also possible that the ClickMax buffer being reached in each monitoring minute being

indicative of the detection of porpoises being affected during the noisiest periods. This was the most

important variable in explaining the variation in detections.

Time of year (Julian Day) – Porpoise detection rate varied significantly throughout the year, peaking

between December and March and lowest between July - October.

POD ID – There was significant variation in porpoise detections caused by the POD that was used.

Figure 6 (right-hand plot) indicates that the different C-PODs used (black lines) had different

detection rates. Detection rates were lowest in unit 1678 and highest in units 1668 and 2035 (the

confidence intervals around these values are shown by the red-marks above and below each black

line). By including this covariate we have tried to control for this effect (i.e. different detection rates)

in the modelling. Therefore we can be confident in the intra-annual variations in porpoise activity

observed.

Figure 6 - Fitted relationships for the model with 95% confidence intervals based on GAM standard errors. Y axes show the partial

residuals for each model covariate generated by regressing the response on the other covariates. A larger y-axis indicates a more

important covariate.

Table 6 – The relative importance of each model covariate in explaining the observed porpoise activity patterns. 1 indicates the

covariate was the most important and 3 indicates the lowest importance.

Covariate decrease in QIC Rank

ClickMax -226.117 1

Time of year -49.3272 2

POD ID -27.5547 3

4.1.2.2 THE (MINOR) EFFECT OF LATITUDE

Whilst latitude was not retained in the final model (indicating it did not have a strong enough effect

on porpoise detection rates given the other covariates in the model), a weak but noteworthy pattern

was observed. As the latitude of the site decreased (i.e. moving south) the detection rates of

porpoises increased (Figure 7). This likely indicates that porpoises are found further offshore.

Figure 7 - Porpoise activity plotted against Latitude. The y-axis is the percentage of days (%DPD) in each dataset with one or more

porpoise positive minutes.

4.2 C-POD PERFORMANCE

Most of the C-PODs worked well during deployments, however three of the units were lost during

deployments (site D (twice), site aA and aC) meaning little or no data were collected from these

sites. Also noteworthy was a problem that occurred during the deployment at site aD. During the

deployment on the 15/10/2012 the C-POD unit appeared to tip over to horizontal (possibly dropping

to the seabed and lying horizontally) and immediately following that a number of false porpoise

detections were made on the unit. During this time three dolphin detections were made and were

validated by Chelonia Ltd – but it was not possible to distinguish the genuine porpoise detections

from the false ones during this period. Therefore porpoise data collected after 15/10/2012 was

excluded from further analyses

4.2.1 EFFECT OF NOISY ENVIRONMENT

The performance of PAM equipment can be impacted by noisy environments. In the case of PODs,

they work by monitoring each minute with a pre-determined ‘buffer’, previously described as the

‘ClickMax buffer’. In each minute the POD will continue to monitor until this maximum buffer is

reached. As described in section 3.4.2, in the C-POD data it was possible to determine how often (if

at all) this ClickMax buffer was reached during each minute of the deployment and as discussed, this

was incorporated into the modelling analysis. From assessing the raw data, it is clear that the C-PODs

deployed suffered from some data loss due to the ClickMax being reached (Figure 8 & 9). The extent

of this issue was strongest during periods of spring tides.

R² = 0.8516

0

10

20

30

40

50

60

50.46 50.48 50.5 50.52 50.54 50.56 50.58 50.6

% o

f D

etec

tio

n P

osi

tive

Day

s

Latitude (decimal degrees)

Figure 8 - Plot showing noise levels with respect to the ClickMax buffer over 3 days. The 'plateauing' indicates the ClickMax is reached

for hours at a time (during which the C-POD is not monitoring effectively - see below).

Figure 9 - A close-up plot of 3 successive minutes of monitoring. The vertical dotted lines indicate the divisions between monitoring

minutes. The bottom panel shows all the clicks (small white dots) by frequency logged in each minute. It is clear that in each minute

period shown, only a few seconds of actual ‘listening’ occurs before the ClickMax buffer is reached and the POD becomes dormant

before waiting for the next available monitoring minute period.

5 DISCUSSION

The results from this study have highlighted some important features relating to porpoise activity

around the Navitus Bay development site. Porpoises were detected on between 17-52% of days in

the study area indicating that porpoises were generally present infrequently in the region

throughout the monitored period. In addition to porpoises only being detected infrequently, overall

usage was low; the percentage of hours containing a porpoise detection (% with DPM) were

consistently low (~1 - 3.6%). Similarly the total number of porpoise positive minutes per day was

extremely low. The porpoise detection rates observed across the Navitus Bay site are much lower

than other C-POD/T-POD studies around the UK (Table 6) indicating that this region is likely less

important habitat than some of the other regions studied. This is supported by other studies (incl.

reviews of existing data) which indicate the south coast of England is not a key area for porpoises

(Reid, et al. 2003; Hammond, et al. 2002; SCANS II, 2005)

Table 6 - A comparison of harbour porpoise detection rates from Navitus Bay with other sites around the UK (using C-PODs/T-PODs.

Detection rates taken from Booth, et al., (2011); Booth & Lacey, 2011 and Sparling & Booth, 2012.

Site Equipment % with DPM MeanDPMpH %DPD

N. Ireland T-PODs 6% 0.164 86%

SW Wales C-POD & T-PODs 9-18% 0.21-0.46 97%

Bristol Channel C-PODs 59% 16.2 84%

Navitus Bay C-PODs 1.99% 0.042 31%

The fact that there were very few confirmed dolphin detections indicates that dolphins are an

infrequent visitor to the area covered by the monitoring. It is important to highlight that PODs are

expected to detect vocalising dolphins out to ranges over approximately 1,250 m (based on studies

elsewhere; T-POD – the C-POD predecessor - in Philpott, et al.(2007) and C-PODs (Meier, 2010)); it is

therefore possible that dolphins do use some parts of the area not covered by the monitoring,

however given the design and spacing of the C-POD array (approximately 3.5 km between sites), it is

expected that there would be a good probability of animals being detected within the area likely to

be affected by the construction and operation of the wind park. This infrequent dolphin presence

across the area monitored indicates that there is a low possibility of dolphin species being disturbed

by piling.

PODs log continuously and are therefore useful for providing continuous data on porpoise activity

within a radius of 2-300m (Tougaard et al. 2006). However it is important to stress that range is likely

to vary depending on the direction of travel of the porpoises and to what extent clicks are produced

off-axis. This is because harbour porpoise clicks are produced with a very narrow beamwidth and so

are highly directional (i.e. an animal pointing away from a C-POD will not be detected) It is also likely

that interference by non-biological sound sources during tidal flows caused high levels of data

clipping and therefore a reduction on the available monitoring time during periods of high tidal flow

(see ‘C-POD Performance’ above) as a strong tidal pattern was observed in the ClickMax.

The modelling approach employed in this study allowed an evaluation of the different factors

affecting porpoise activity across the monitoring locations and allowed us to assess the relative

importance of each factor in explaining the observed variations in porpoise activity.

CLICK MAX

The Noise proxy (‘ClickMax’), the number of clicks recorded in each monitoring minute, was

determined to be the most important factor. As noise levels increased the porpoise activity

decreased dramatically, suggesting that either animals were avoiding noisy areas or periods or that

ambient noise conditions impacted the ability for porpoises to be detected using the C-PODs (i.e.

high noise causing the POD to reach its click maximum before the minute of monitoring was

completed). The inclusion of this covariate is an effort to ‘control for’ any issues caused by noise. In

addition, although it was retained in the best model, noise was determined to be of relatively low

importance in explaining the patterns observed.

The ClickMax setting used during the Navitus Bay deployments was the default setting of 4096. This

can be set higher, allowing more flexibility in monitoring. Any future deployments should consider

setting this to a higher level and deploying for shorter periods (because by setting a higher buffer

level, the memory gets filled up quicker with more clicks, necessitating the need for more frequent

service visits). It is important to note that selecting the ‘ClickMax’ is a trade-off between maximising

the amount of monitoring time in each minute (as discussed it is possible for the ClickMax to be

reached in <1minute in noisy environments) against running out of storage space before the C-PODs

are retrieved (and losing valuable monitoring time). Here the default value of 4096 clicks per minute

was selected as the ClickMax to allow for service visits to be conducted on a 2-3 month rotation.

However, it is noted from the results of this deployment that porpoise and dolphin detections are

very low, and further deployment is unlikely to achieve any significant addition to the analysis.

TIME OF YEAR

Significant monthly variations in porpoise activity were observed in the data as ‘Julian Day’ was

found to be an important covariate in explaining the observed patterns. A number of other studies

have determined that porpoise activity varied throughout the year. In this study, detection rates

were highest in winter and spring and lowest in the late summer and autumn periods. Previous

studies have investigated seasonal variations in porpoise activity and patterns appear to vary

between locations. At some sites there were peaks of porpoise activity in summer months and at

others peaks occurred during winter and spring months. Studies in Cardigan Bay have observed

variations in the number of porpoise positive days depending on the month with this metric being

highest in winter and spring and lowest in summer and autumn (Simon, et al. 2010). Significant

seasonal variations with activity peaks during summer months (and lows in winter months) were

observed in TPOD studies in the Baltic Sea (Verfuß, et al. 2007). In SMRU Ltd studies of harbour

porpoise in Strangford Lough, porpoise activity varied seasonally, with peaks of approximately equal

magnitude in spring and autumn and the lowest detections in the middle of summer (Booth, et al.

2011). The patterns observed here do not match with other studies, but as is noted above, site-

specific variations in presence are common for the species.

As no significant numbers of detections of dolphins were found, no seasonal patterns could be

determined.

POD ID

Results showed that there was significant inter-C-POD variability that influenced the detection rates

of harbour porpoises during the study. This means that while we cannot adjust detection rates

between different units by a ‘variability’ metric, we know that the impact of the use of different C-

POD units has been ‘controlled for’ in the models and therefore we can be more certain about the

conclusions drawn with respect to other model covariates. Furthermore it was identified that

biological factors, e.g. time of year, were more important in explaining the observed porpoise

detection patterns.

DISTANCE FROM LAND

This study observed a weak gradient in porpoise detections with distance from land. As distance

from land increased (covariate ‘latitude’ in the models), the porpoise detection rates also increased.

Other studies have indicated a relationship with distance from land, however the opposite pattern

was observed – with detection rates decreasing offshore (e.g. Scheidat, et al., 2012; Booth, et al.

2013). It could be that animals typically do not come in so close into the bay, but merely transit

through the region using offshore waters.

As no significant detections of dolphins were found, no spatial patterns could be determined.

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