DOI: 10.5061/dryad.g1jwstqv1
title: Niche variation in sympatric delphinids: Indo-Pacific bottlenose and Indian Ocean humpback dolphins on the south-east coast of South Africa

description: The ecological interactions and mechanisms that mediate coexistence among
 species are often poorly understood, despite its relevance to conservation
 management. A case in point is the Indo-Pacific bottlenose dolphin
 (Tursiops aduncus) and Indian Ocean humpback dolphin (Sousa plumbea) that
 occur sympatrically along the south-east coast of South Africa. To improve
 on our understanding of their coexistence, data on spatial distribution
 and habitat use were collected using boat-based surveys during 2014-15
 along 145 km between Goukamma and Tsitsikamma MPAs. During 235 hours of
 survey effort, 55 and 42 encounters with T. aduncus and S. plumbea were
 recorded respectively. We investigated differences in their space use and
 habitat preferences. There was a strong overlap of the core areas used by
 both species, located mainly along Goukamma MPA and the eastern section of
 Plettenberg Bay. For both species, foraging was observed in mornings and
 travelling in afternoons. T. aduncus showed a preference for rocky areas
 in the afternoon while S. plumbea preferred estuarine and sandy habitats.
 We demonstrated that coexistence among dolphins with overlapping
 distributional patterns can be driven by fine-scale spatial segregation in
 feeding activities. Spatial conservation management measures such as the
 protection of sandy and estuarine habitats are recommended.
description: <strong>Study area and
 data collection  </strong> Boat-based surveys for dolphins were conducted
 along the south coast of South Africa within the Agulhas Bioregion in
 2014–15. When a dolphin or groups of dolphins were encountered, they were
 identified and photographed, the position of the sighting was recorded and
 the behaviour of the dolphins were categorised. The surveys occurred
 between the western boundary of the Goukamma MPA and the eastern boundary
 of the Tsitsikamma MPA (Figure 1). The area is strongly influenced by the
 warm Agulhas Current [22] and wind-driven upwelling results in high
 levels of primary productivity and associated high prey biomass for
 predators [22].  <br>
 The surveys were designed as a
 transect line of 145 km distance, running parallel to the coast
 (approximately 100 m from the coastline), corresponding with the known
 coastal preferences of both dolphin species [16,23]. A full survey was defined as transect
 completed within a calendar month between April 2014 and October 2015 (n:
 13, Table 1). This ensured that the survey effort was similar throughout
 the study site. Survey effort was calculated in hours and encounter rate
 was calculated as the number of encounters per hour searched. Survey
 effort was discontinued when the Beaufort sea-state was above 3, when the
 boat was in transit and during encounters. Surveys were performed at a
 speed of eight knots with at least two or three experienced
 observers. A group was defined as two or more animals
 within a 100-m radius of each other, with coordinated activities
 [24]. In addition to the GPS coordinates of the
 encounters, sea surface temperature (SST) depth and time of day were
 recorded. Five behavioural categories for <em>T</em>.
 <em>aduncus</em> were defined according to
 [25]: travelling, foraging, socializing, milling or
 resting. Four behavioural categories were defined for
 <em>S</em>. <em>plumbea</em> according to
 [26]: travelling, foraging, socializing or resting.
 See [27] for further information on study area, survey
 design and the full data collection procedures.
 <strong>Data
 analysis</strong> <em>Spatial and behavioural
 analyses</em> The spatial distribution of
 <em>T</em>. <em>aduncus</em> and
 <em>S</em>. <em>plumbea</em> was assessed by means
 of a kernel density estimator (KDE) analysis [28]. This probabilistic technique provides
 estimates of the utilisation distribution (UD), a probability density
 function that describes the relative use of space by an animal within a
 defined area, based on a sample of animal locations [28]. The geographical coordinates of each group of
 animals at the time when they were first encountered provided the spatial
 data for the analysis. A bivariate normal kernel UD was used to determine
 the home ranges (95% UD) and core areas (50% UD) for each species. The
 probability of occurrence was calculated using smoothing parameters for
 the kernel using the least squares cross-validation (LSCV) method
 [29]. The LSCV method examines various smoothing
 parameters and selects the bandwidth that gives the lowest mean integrated
 squared error for the density estimate [30]. KDE analysis was performed using program R
 1.0.143 [31] with ‘adehabitatHR’ package, v. 0.4.14
 [32]. To assess spatial segregation between
 <em>T</em>. <em>aduncus</em> and
 <em>S</em>. <em>plumbea</em>, we calculated a UD
 overlap index (UDOI) to quantify the degree of overlap between the home
 ranges of the two species [33]. A randomization approach was then applied
 whereby the null hypothesis that there is no difference in spatial
 distribution between the two species, was tested [34]. For the null hypothesis to be upheld, there
 should be no significant differences between the degree of overlap
 calculated for the original home ranges and the degree of overlap
 calculated for home ranges for which species were randomly assigned. Home
 ranges were generated from 1000 randomly generated datasets in which
 either species was randomly allocated to each location. P-values were
 calculated from the proportion of random overlaps that were smaller than
 the observed overlap. Behaviour and habitat preference were assessed
 further in relation to the core areas (i.e. inside and outside 50% UD).
 Pearson’s chi-square homogeneity tests [35] were used to investigate if behavioural states
 varied spatially in relation to the core areas and time of the day
 [20].
 <em>Habitat preference
 and temporal segregation</em>
 To assess habitat preference
 (when first encountered) and potential temporal segregation of both
 species, 2 km<sup>2</sup> grid cells along the coast were
 created using QGIS 2.18.4 [36], resulting in 73 grid cells in total. Each cell
 was characterised according to the benthic substrate, presence or absence
 of reefs, mosaic (a combination of reef and seafloor) or estuaries, and in
 relation to the boundaries of MPAs (inside or outside). Within the study
 area, three broad types of benthic substrate exist, namely rocky, sandy
 and mixed coast (i.e. rock and sand). The benthic substrate types were
 obtained from the benthic and coastal habitat map of the National
 Biodiversity Assessment [37]. Grid cells were also characterized for each
 survey according to broad time of day (AM/PM), season, sea surface
 temperature (SST) and the occurrence of <em>T</em>.
 <em>aduncus</em> and <em>S</em>.
 <em>plumbea</em> (calculated as the presence-absence of
 dolphins in the 2 km<sup>2</sup> grid cell during each
 survey). Seasons were defined as (1) summer = December-February, (2)
 autumn = March-May, (3) winter = June-August, and (4) spring =
 September-November. Occurrence (presence-absence) data of
 <em>T</em>. <em>aduncus</em> and
 <em>S</em>. <em>plumbea</em> as a function of the
 spatial and temporal characteristics of the grid cells and surveys were
 first visually assessed for normality and then analysed separately, using
 a generalised linear mixed-effects models (GLMM) with binomial
 distribution (link function: logit). Analyses were conducted using the
 ‘lme4’ package v. 1.1-12 [38] in the freeware R 1.0.143 [31]. For each model, all predictor variables were
 included in the analysis: relation to MPA boundaries, benthic substrate,
 presence or absence of reefs, mosaic and estuaries, and also an
 interaction term between benthic habitat and broad time of day. The 2
 km<sup>2</sup> grid cell was included as a random intercept to
 account for repeated measures and models were fitted by restricted maximum
 likelihood estimation (REML). Collinearity between all covariates was tested.
 The Variance Inflation Factor (VIF) scores were calculated for each
 predictor variable using the ‘vifcor’ function of the ‘usdm’ R package v.
 1.1-15 [39]. Only uncorrelated covariates (VIF &lt; 3)
 were included to avoid misinterpretation of the model
 [40]. Model selection was based on the Akaike
 information criterion scores corrected for small sample sizes (AICc),
 whereby all realistic permutations of predictor covariates were fitted to
 separate models under maximum likelihood (ML) estimation using the
 ‘dredge’ function in the ‘MuMIn’ package v. 1.15.6 [41]. All models with a ΔAIC &lt; 2 were
 considered as candidate subsets of the most parsimonious models. Candidate
 subsets were then used to generate model-averaged coefficients
 [42]. As a measure of the goodness-of-fit, pseudo R2
 values were calculated for the model to explain the proportion of variance
 accounted for by the fixed and random factors [43].
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description: R programme and Microsoft Excel