Aplicación de la entrevista como técnica de recogida de datos

Distance sampling necessitates some core assumptions to be met (Buckland et al., 1993). The preliminary assumption being that if bottlenose dolphins were on the trackline they will be detected, i.e. g(0)=1 (Buckland et al., 1993). Despite the low height of observers above water level, it was assumed this assumption was met (or minimally violated) due to the concurrent data collected on dive time of bottlenose dolphins in Far North waters (average 57.7 s, n = 1200 dive cycles for coastal bottlenose dolphin and average 137.2 s, n = 900 dive cycles for oceanic bottlenose dolphin; C. Peters, unpublished data). Additionally, a range of group sizes were collected across all distance bands and surveys were only undertaken in optimal sea conditions (as per Ronconi & Burger 2009). Steps were thus taken to minimise bias due to missed detections on the transect line (as per Flach et al., 2008). It is worth noting, if a bias did occur the estimates (density and abundance) produced would be negatively biased. The estimates in this body of work should be viewed as conservative in light of this.

Chapter 2 – Density, abundance and distribution of parapatric common bottlenose dolphin (Tursiops truncatus) ecotypes in Far North waters, New Zealand

A further assumption is that no undetected movement occurred before being sighted and no groups are recorded multiple times in a single sample (Buckland et al., 1993). During this study, responsive movement was detected though it was minimal (apparent observations of being attracted to or avoiding the survey vessel were rare (15 %, n = 43 for coastal bottlenose dolphin (n=228) and 0 % for oceanic bottlenose dolphin (n = 47); C. Peters, unpublished data). In the Bay of Islands particularly, the dolphins appeared to be habituated to vessels (Peters & Stockin 2016). Nonetheless, due to the use of closing mode the possibility of double counting the same group needed to be considered. To minimise this, focal group movement was monitored until survey was resumed. If previously detected groups crossed ahead of the vessel on the transect, the detection was removed from the dataset (as per Flach et al., 2008). Additionally, to add another level of independence, all sightings from the same day in the same stratum were cross referenced from Photo-identification and removed if the same individuals were present, a benefit of closing mode.

The final key assumption is that an errorless measurement of angle, distance and group size is achieved (Buckland et al., 1993). This assumption relies as much on training and/or experience of observers as it does with the equipment utilised (as per Flach et al., 2008). Recording angles from a small vessel may have added operational error, due to the increased likelihood of drift compared to a larger boat (as per Flach et al., 2008). To minimise this error recordings were taken as efficiently as possible and without rounding (assessed for all measurements in a scatter plot as per Flach et al., 2008). The use of systematic training in sighting distance estimation reduced distance measurement errors as a good indication of sighting location allowed for the rapid recording of group distance. Additionally, no signs of ‘heaping’ were detected during initial data exploration (Buckland et al., 1993). Finally, the use of distance bins in this study reduced possible bias in distance estimates, as recommended by Buckland et al. (1993). Model-based deductions are singularly appropriate due to the input accuracy and the appropriate scale of application. The distance-sampling methodology is no different. While covariates considered here are not exhaustive of those that may affect dolphin distribution, coarse-scale factors have been focused on due to the intended use of the estimates produced. For density surface models, utilising a bivariate spline term, and including location of a grid where a bottlenose dolphin group was detected, offers an adaptable and comparable technique for detecting spatial variation. Note that classifying the drivers of the spatial variation was not attempted (as per MacKenzie & Clement, 2014). A wider set of factors was not included in this

Chapter 2 – Density, abundance and distribution of parapatric common bottlenose dolphin (Tursiops truncatus) ecotypes in Far North waters, New Zealand

70 study’s density surface models, as the distribution modelling objective was to describe location, but not understand habitat selection of bottlenose dolphins.

Oceanic bottlenose dolphins were frequently observed in wider survey, but rarely encountered in sheltered areas of Bay of Islands and Cavalli Islands, and never in Whangaroa Harbour or Doubtless Bay. Unfortunately, seasonal occurrence resulted in a small sample size for oceanic bottlenose dolphins. This made any meaningful stratum-specific statistical analyses impossible, resulting in a limited discussion of oceanic bottlenose dolphin distribution. To obtain reliable estimates, it is suggested a minimum of 60 – 80 detections are required to calculate meaningful estimates from transect surveys (Buckland et al., 2001), which was not achieved in Far North waters for oceanic bottlenose dolphins. Additionally, the oceanic bottlenose dolphin groups sighted were not necessarily truly independent, as multiple sightings occurred in a single day and baseline data are not available to inform on the spread of inter- and intra-specific groupings. Applying standard group definition criteria was the only way to maintain comparability across ecotypes, though this may result in some large or very spread groups being classified as multiple detections. In fact, 34 groups were detected as a result of 30,728 km of on survey effort, supporting the notion that occurrence patterns were not artefacts of effort. In studies of transient or rarely occurring species, this is often a limitation. For a detection function estimate, including sightings from off effort, deeper water or high-density areas identified in this study might improve estimates, though these data cannot be incorporated into abundance estimates (Williams & Thomas, 2009).

Implementation of the survey design was successful. However, the detection function did decrease with increased distance as a result of covariates. Nonetheless, the CVs of this study fall within the range of comparable abundance studies (e.g., 32.0 % harbour porpoise (Phocoena phocoena, Williams & Thomas, 2007); 48.1 % bottlenose dolphin (Barlow, 1995) and 35.3 % Pacific white-sided dolphin (Lagenorhynchus obliquidens, Williams & Thomas, 2007; as summarised in Dick & Hines, 2011), rendering discussion within the body of literature appropriate. It must also be considered that in this study a complete survey of most regions was completed within days. It is thus not achievable to quantify the duration of shifts, coincidental overlap with surveys, or identify true seasonal patterns. Subsequent surveys over multiple successive timeframes would provide verification beyond occurrence. Additionally, a conclusive driver of the seasonal shift observed for both ecotypes cannot be determined as a result of a data paucity on prey movements. This topic merits additional consideration due to

Chapter 2 – Density, abundance and distribution of parapatric common bottlenose dolphin (Tursiops truncatus) ecotypes in Far North waters, New Zealand

possible implications on the movement of bottlenose dolphin populations and other top predators, and their management. Further to this, while all strata were used in all seasons, distribution and density in wider survey was not uniform along the coast for coastal bottlenose dolphins. The wider survey region of the coast between Bay of Islands, Cavalli Islands and Whangaroa Harbour appears to be important for groups. No sightings occurred between Whangaroa Harbour and Doubtless Bay. Sightings that occurred in wider survey consisted of smaller groups in a travelling behaviour state, possibly reducing detectability compared to large and socialising groups. This suggests the area could be used to transit between sheltered bays, however, the increase in abundance estimates cannot fully account for declines in all other

strata.

Coastal bottlenose dolphin abundance estimates utilised both density surface models and non- density surface models for survey data 2013 – 2015 across seasons and Far North waters strata. Thus, both the historic abundance estimates achieved for the Bay of Islands (Tezanos-Pinto et al., 2013) and the methods employed there are not directly comparable to one another. Additionally, a direct comparison of density surface models and non-density surface models estimates is slightly impeded by the differences noted for some strata. The smooth density surface utilised likely resulted in these differences (MacKenzie and Clement, 2014a).