EVALUACIÓN PROYECTOS DE GESTIÓN E INTERVENCIÓN EDUCATIVA

All oral notes were transcribed onto a spread sheet using the marker indicated on the commentary track. Each acoustic track was cut into 1 minute segments in WavePad Sound Editor (Version 6.59; NCH 2016). Any recordings less than 1 minute or segments cut from the end of tracks that were less than one minute were removed from analysis. Segments that were recorded during or 20 minutes after the dolphins interacted with another vessel were also excluded from analysis following behavioural procedure (Chapter 3; Section 3.2.3). Each 1 minute segment was then imported into Luscinia (Version 2.16.10.29.01; Lachlan 2007) as a ‘song’. Each ‘song’ was viewed on a sonogram, displayed using a frame length of 4.2ms, 76.2% overlap and smoothed using a Hamming window.

4.2.4.1 Whistle rates

A visual representation of the signal was observed as a spectrogram as the sound is played back to count the number of whistles and harmonics for each track. The number of whistles was determined by counting the number of whistles displayed in the spectrogram combined with faint whistles detected audibly. This allowed distant sounds to be picked up by ear that would be missed via inspection of the spectrogram alone. Analysis was restricted to the human audible range for reliability, only whistles with fundamental frequencies below 24kHz were used for analysis. Whistle fundamental frequencies rarely exceed 25kHz, and thus it was assumed that the omission of whistles exceeding this frequency would not overtly bias the dataset (Caldwell et al. 1990; Boisseau 2004). Whistle rates were calculated in two different ways; firstly, the mean number of whistles counted in each minute (mean number of whistles per minute); secondly, by dividing the mean of the number of whistles counted in each one minute segment by the number dolphins recorded in the focal group (mean number of whistles per minute per dolphin).

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Group sizes were categorised into three groups (1-22, 21-50 and 50+ individuals). Whistles that occurred while two or more behavioural states were reported were removed from analysis as it could not be determined which behaviour was influencing the whistle repertoire. Statistical analysis was completed in R Studio (version 1.0.153). A partial autocorrelation function was used to check the data for independence. If whistle rate was not independent, then an autoregressive model was applied to the data to account for autocorrelation. An ANOVA was used to test if whistle rate was significantly different between each behavioural state and each group size category. If an ANOVA test showed significant differences between means, a Tukey’s post hoc test was conducted to identify where significant differences occurred. The number of whistles per minute per dolphin was visualized by plotting the number of whistles recorded per dolphin for each minute of recording, for each behavioural state and group size category.

4.2.4.2 Whistle parameters

Each whistle with a suitable signal-to-noise ratio was considered a “good” whistle, and manually marked in Luscinia by tracing around the whistle for further analysis as an ‘element’ (Edelaar et al. 2012; Figure 4.1). Harmonics were not included when marking whistles, as it was determined that this resulted in more accurate whistle parameter measurements.

Figure 4.1 Example of a traced (green) bottlenose dolphin whistle in Luscinia. Noted: Only

the fundamental frequency is traced.

Parameters for each traced whistle were exported from Luscinia using the Analysis function, into an excel spreadsheet. The means, standard deviations and coefficients of variation were calculated for each whistle parameter to provide descriptive statistics of

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the whistle repertoire at GBI. Whistle parameters extracted by Luscinia (displayed in figure 4.2) include; duration (s), maximum frequency (kHz), minimum frequency (kHz), mean frequency (kHz), beginning frequency (kHz), and ending frequency (kHz). Additionally, the number of inflection points was manually counted from the spectrogram and the range was calculated as the difference between the minimum and the maximum frequencies (Azevedo et al. 2007). Tables displaying descriptive statistics were created to show if and how parameters vary by whistle type, behaviour and group size.

Figure 4.2 Sample of a bottlenose dolphin whistle in Luscinia, highlighting the parameters

that are measured for analysis. 4.2.4.3 Whistle type

Luscinia was used to partially classify whistles into categories using the dynamic time- warping algorithm. The algorithm searches for an optimal alignment between two time series on the basis of the Euclidean distance between acoustic features (Lachlan et al. 2014): in the analysis, these features were spectrograph measures of whistles: time, fundamental frequency and fundamental frequency change. Each whistle was further matched by eye into eight defined types (Table 4.1).

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Table 4.1 Description and example of whistle types of bottlenose dolphins (Tursiops

truncatus) at Great Barrier Island, New Zealand (based on Azevedo et al. 2007; Lopez 2010).

Code Name Inflection

Points Example

Asc Ascending (or

Rise) 0 Desc Descending (or Falling) 0 Sine Sinusoidal 2 Desc- asc Descending– Ascending (or Concave) 1 Asc- desc Ascending– Descending (or Concave) 1

Constant Constant (or

flat) 0

Multi Multi-looped 4-19

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The percentage of each whistle type recorded was calculated to determine the most and least frequently recorded whistles overall. Chi-squared tests were performed to identify whether whistle type was correlated to behaviour and group size. The overall percentage of each whistle type was plotted by behavioural state and group size categories.

4.3 Results

4.3.1 Effort

Between July 2015 and March 2016, four dedicated boat-based surveys were conducted at GBI, resulting in a total 1511.1km of track around the western coast of GBI in ca. 139h. During this time, a total of 21 encounters with bottlenose dolphins occurred, totalling ca. 66h of observations.

A total of nine independent dolphin social groups were acoustically recorded. Group size varied from 6 to 65 individuals, with a mean of 33 (SD=18.4). All groups contained at least a single calf or juvenile and acoustic data were recorded through all austral seasons. As all groups recorded contained calves or juveniles, it could not be tested how their presence/absence affected whistle rates or types. Surveys resulted in 14h of acoustic recordings, in which 14,358 frequency modulated whistles were detected. A total of 7,606 whistles with good signal to noise ratio were analysed.