Data collection

Sperm whale data were collected simultaneously to the dolphin distribution data as detailed in §2.2.1. Real-time detection and tracking of sperm whale clicks detected by the hydrophone array was carried out using the IFAW software packages Rainbow Click (Gillespie 1997; Leaper et al. 2000). This software detects and measures bearings to transients such as sperm whale clicks and was run continuously whenever the hydrophone was deployed. In addition, every 15 minutes an experienced monitor actively listened to the hydrophone for 1 minute (a listening station), scoring for boat noise from the survey vessel, water noise, remote vessel noise, and sperm whale clicks on a subjective scale of 0 (inaudible) to 5 (loud). Since sperm whales click at a regular interval usually from different directions, it was usually possible to distinguish the number of sperm whales vocalising, which was also noted during the listening station. If there were too many whales present to determine the number vocalising, the number of individual tracks identified by Rainbow Click were counted and noted instead. Two acoustic monitors rotated the listening shifts within survey trips (usually alternating every 6 hours over a 24 hour period), and on all surveys at least one of three specialised acoustic monitors was present to maintain consistency in sound level measurement.

3.2.2 Environmental data

The environmental variables used were the same as those described in §2.2.2, and included survey variables, temporal variables, topographic variables, satellite surface environmental variables, and Forecasting Ocean Assimilation Model (FOAM – Bell et al. 2000) oceanographic variables. Survey variables included vessel speed, survey

vessel noise, remote vessel noise, and water noise (Table 2.2). Temporal variables included time of day (calculation given in §2.2.2), and month where 1 = May, 2 = July, and 3 = September & October. Topographic variables only included depth. Satellite variables included Sea Surface Temperature (SST), and surface chlorophyll (Table 2.2). FOAM oceanographic variables included SST, thermocline depth & strength (where thermocline strength is calculated as the difference in temperature between surface and bottom waters), halocline depth & strength (where halocline strength is calculated as the difference in salinity between surface and bottom waters), surface and bottom current speeds (Table 2.2).

3.2.3 Acoustic data analysis

Sperm whales can be heard over large distances, resulting in the same whale being heard over several listening stations, causing serial autocorrelation (or

pseudoreplication) and lack of independence between sampling units. Ideally, the tracks of all sperm whales would have been analysed and the distance to the track line estimated for every animal: only those animals within a certain range of the

hydrophone would be included in the analysis. Due to the time consuming nature of this process, a small sample of 30 sperm whale tracks was analysed and a rule for deriving appropriate sampling units from series of listening stations was developed. This rule was based on minimising the number of stations over which an individual sperm whale could be heard, resulting in sampling units which were independent with respect to individual sperm whales.

To give an indication of the time involved in processing just this small sample, 30 tracks took around two weeks to manually select the clicks for each of the individual sperm whale tracks, export to R and run the script to estimate the time at which the animal passed abeam, and to estimate the distance of the animal to the trackline. This sample formed less than half of one survey, so each survey would take at least a month to process dependent on the number of sperm whales heard.

To determine a rule to make listening stations independent, the acoustically scored levels of sperm whale clicks (0-5) and the number of individuals heard were

compared with the bearings obtained from the same period of time for clicks detected by the Rainbow Click software, and the number of individual tracks. This was carried out for data collected over a range of different habitat types (Faroe-Shetland Channel

and Rockall Trough). The whale’s location was assessed by eye based on the crossed bearings and the range of this from the trackline was measured (in a similar manner §2.2.3). From this it was also possible to estimate the time at which the animal passed abeam, and hence which listening station it corresponded to. This technique has been used to estimate whale locations in other studies using similar towed hydrophones e.g. Leaper et al. (2000) and Hastie et al. (2003). Sperm whale clicks could usually be resolved as trains from individual whales rather than groups, so the number of individual tracks at different bearings represented the number of individuals vocalising within range of the hydrophone (Figure 3.1).

Figure 3.1 – Rainbow Click bearing-time plot of clicks recorded from the RSS Darwin on 12th October 2005. Time is shown along the x-axis (total length of 20 minutes displayed), and bearing in degrees along the y-axis from 0º (ahead of the vessel) to 180º (behind the vessel). The black line through the middle of the display represents the 90º line, indicating any clicks heard abeam of the vessel. The two parallel tracks at the top of the display remaining at the same bearing for the duration of the time sample are noise clicks originating from the survey vessel. All other tracks of black dots are the clicks of individual whales of which there are over 15 animals within the time sample shown here. Tracks with steeper slopes crossing the 90º abeam line are closer to the vessel than those with shallower slopes.

Based on crossing bearing of clear continuous tracks of sperm whale clicks identified using the Rainbow Click software, distances from the trackline ranged from 700 m to 12 km from the vessel at an average of 4.7 km (sd = 2.6 km), though sporadic clicks were occasionally measured at ranges of up to 23 km. Most acoustic detections of sperm whales were made in groups spread over a large area (average 32 km assuming an average vessel speed of 10 knots). The largest group was spread out over 60 km in the Rockall Trough and comprised a large number (estimated to be > 30) of individual

sperm whales. This results in severe serial autocorrelation in the data, not only due to the large distance over which detections could be made, but also due to the large group sizes or ‘super aggregations’ (Jaquet & Gendron 2002).

Based on the sample of data including 30 sperm whale tracks, a ‘true distribution’ of sperm whales was generated. If a sperm whale passed abeam of the vessel during the 15 minute period centred at the time of the listening station (i.e. within 7.5 minutes before or after the start of the 1 minute listening station), then the sperm whale was associated with that listening station. This ‘true distribution’ was compared to the acoustically scored levels of sperm whale clicks (0-5).

Different rules were applied to the acoustically scored levels of sperm whale clicks to classify the levels as a presence/absence of sperm whales:

i) Presence when acoustically scored level of sperm whale clicks >0, absence when = 0

ii) Presence when acoustically scored level of sperm whale clicks >1, absence when ≤ 1

By comparing the distributions resulting from application of these rules with the ‘true distribution’ it was possible to calculate the percentage correctly classified. Using all sperm whale vocalisations (i.e. ≥ 1) resulted in only a 45% correct classification rate. Whereas, excluding the quiet sperm whale vocalisations (≤ 1) predicted the ‘true distribution’ of sperm whales correctly 65% of the time. Therefore, this latter rule (rule i) was used to generate the data on which subsequent analyses were based. This method removed much of the autocorrelation in the data caused by hearing the same animal over several listening stations, but will not have removed the autocorrelation due to large group sizes.

Previous studies of sperm whale distributions (Gannier et al. 2002; Gannier & Praça 2007; Gordon et al. 2000) have eliminated autocorrelation by considering each series of consecutive positive segments as a single group presence, centred in the middle of the group. This method is useful for creating independent samples, but raises

questions about handling environmental data recorded in areas where the whales were actually present, but not considered the centre of the group. For this analysis it was

therefore decided to allow serial autocorrelation due to large group sizes (which would tend to produce overfitted models), and remove the resultant overfitting of the model selection process by carrying out cross-correlation on an independent data set (detailed in §3.2.4.1). Aarts (2006) used a similar method to remove overfitting of models resulting from serial autocorrelation in individually tracked grey seal (Halichoerus grypus) locations to predict their habitat preferences.