General social structure

Gulf of Trieste dolphins appear to live in two general kinds of social units: (a) large mixed-sex groups with strong, long-lasting associations and (b) small groups with weaker, temporally unstable associations. This does not appear to be age-dependent. Two largest clusters featured strong bonds, while seldom interacting with the other cluster. This structuring was also evident in the field. These two clusters showed high levels of group stability, which persisted through the study years and beyond (T. Genov, personal observation), although exact group membership could vary. Gregariousness, connectedness and strength of associations (indicated by HWI, Affinity, Clustering coefficient, Eigenvector centrality and Strength) were quite high and relatively similar between the two, as was the number of associates (Reach; Table 2). In contrast, these metrics were substantially lower in cluster C, where animals showed no strong association preferences. Because they were occasionally observed with animals from other clusters, their Closeness was highest (Table 2).

When including all associations (Fig. 4a) the network was reasonably well-connected, with no individual ‘bottlenecks’ between clusters, which were inter-connected via several but not particularly numerous individuals. Such ‘social brokers’ (Lusseau and Newman 2004) may maintain population cohesiveness and prevent complete cluster isolation, possibly having disproportionate influence on the population connectedness, as found in killer whales (Williams and Lusseau 2006), macaques (Flack et al. 2005) and squirrels (Manno 2008). However, when considering only ‘meaningful’ associations, greater than twice the mean HWI (Durrell et al. 2004; Gero et al. 2005; Wiszniewski et al. 2012), structuring becomes striking and clusters completely separated (Fig. 4b).

Associations were temporally relatively stable (as supported by SLAR and field observations), although stability varied with different levels of social organisation. Cluster A in particular (but also B) seemed to contain ‘core’ membership (first-level unit) and other ‘tiers’ that joined core members to form higher-level units. In such multi-level systems, seen also in African elephants (Wittemyer et al. 2005) clusters can sub-split during times of ecological constraints and fuse again when conditions are favourable or promote cooperation. We sometimes observed cluster A dolphins forming smaller groups (≤ 10), which often joined into groups of 30 + animals. Group composition during encounters was also surprisingly stable, more than in the closest other known local population in the Adriatic Sea (Bearzi et al. 1997) or in most other populations worldwide (Connor et al. 2000; Lusseau et al. 2006). Once encountered, groups were unlikely to change during observations, which could last several hours (Genov et al. 2008). This population is rather small (Genov et al. 2008; Genov 2011) and some authors hypothesised that community size influences group stability in fission–fusion societies, with smaller communities leading to decreased fission–fusion flexibility (Lehmann and Boesch 2004; Augusto et al. 2012).

In several Tursiops populations, social structure involves sex/age segregation (Wells et al. 1987; Connor et al. 2000; Fury et al. 2013). Here, structuring did not appear sex-related, as clusters contained both sexes. We found no evidence of male alliances. Although male–male associations were stronger than male–female or female–female associations, this was not significant, with stronger male–female than female–female associations. Most encountered groups contained both sexes (regardless of season), which suggests that mixed-sex groups were not related to reproductive state. Likewise, although more than half of all groups contained calves, adult-only groups were common. Reproductive state or presence of calves, therefore, fails to explain these patterns.

Presence of large mixed-sex groups resembles Doubtful Sound bottlenose dolphins in New Zealand (Lusseau et al. 2003). Lusseau et al. (2003) hypothesised that ecological constraints, such as variable productivity, drive social organisation. In such environments, groups may need to rely on individuals with long-term knowledge about spatio-temporal distribution of prey sources, which might explain lack of sex segregation and greater population connectedness (Lusseau et al. 2003). The northern Adriatic is characterised by large spatio-temporal variability in nutrient input and productivity (Fonda Umani et al. 2005; Mozetič et al. 2010, 2012), and our study area contains relatively uniform bottom topography. With lack of major prey-aggregating bottom features, spatio-temporal distribution of prey is likely highly variable, which may promote network connectedness. Clusters A and B both contained individuals which appeared ‘older’ based on their external appearance. These animals may possess long-term knowledge needed to tackle such constraints and thus play a key role in their community.

Temporal segregation

Several studies found spatial segregation in Tursiops (Chilvers and Corkeron 2001; Chilvers et al. 2003; Lusseau et al. 2006; Fury et al. 2013; Carnabuci et al. 2016). In Moray Firth, Scotland, this segregation appeared season-dependent (Wilson et al. 1997). During summer, part of the population moved into inner parts of the Firth and was replaced by dolphins from outer parts. However, clusters in our study overlapped spatially, but not temporally, and we found differences on a daily, rather than seasonal level. Such intraspecific diel temporal partitioning does not appear to have been documented in cetaceans previously, nor in other mammals (Kronfeld-Schor and Dayan 2003), with one exception recorded in the use of running wheel in captive mice (Howerton and Mench 2014). Whether this pattern results from competitive exclusion, avoidance of aggressive interactions, or different foraging tactics, remains unknown. Given that prey resources in the marine environment are patchy and variable, prey resource defence is not a likely explanation (Ramp et al. 2010). Lack of sex segregation also dismisses access to females as an explanation. We are currently working to determine if genetic relatedness correlates with the social partitioning observed here.

We considered potential confounding factors. If the distribution of cluster A was linked to trawlers, which only operated during certain hours, this would explain the pattern. However, pair trawlers operated in the morning and afternoon, and bottom trawlers operated day-long (including evenings). Cluster A regularly used trawling areas even in the absence of trawlers, with no difference in group composition. More importantly, no trawlers operated in the core study area. Finally, cluster A dolphins did not always follow trawlers, even if trawlers were around. Trawlers, therefore, fail to explain temporal partitioning.

We also considered lower sample size for cluster B. Caution is needed when making inferences from small sample sizes, but temporal patterns here appear quite striking. The presence of a temporal (rather than spatial) pattern suggests the observed associations were not an artefact of space use (animals being together just because they use the same space), but due to genuine social preferences. Further, due to long-term and extensive survey effort (Table 1, Fig. 1), this pattern is unlikely to be an artefact of effort. Surveys in recent years (2012–2017, analysis pending) further support this, with both clusters continuing this pattern, and even occurring in the same area within a single day, but at different times (Morigenos, unpublished data).

Finally, it remains to be determined if segregation is specific to this area, or if it occurs in other areas used by the animals. The range of this local population is unknown (Genov et al. 2016), but evidence from photo-ID (Genov et al. 2009) and genetics (Gaspari et al. 2015) suggest it is a distinct unit.

Interactions with trawlers

Two clusters displayed behavioural differences related to trawling. Cluster A dolphins often interacted with pair trawlers and occasionally bottom trawlers, while cluster B dolphins did not (‘trawler dolphins’ vs. ‘non-trawler dolphins’, Chilvers and Corkeron 2001). Fishing has a major impact on cetaceans worldwide, not only through incidental mortality (Read et al. 2006), but also through prey depletion (Bearzi et al. 2008), habitat degradation (Turner et al. 1999) and ecosystem change (Worm et al. 2006). More subtly, fishing activities can affect, or be affected by, cetacean behaviour. In Queensland, Australia, bottlenose dolphins were found to form two communities, where one fed in association with trawlers and the other did not (Chilvers and Corkeron 2001; Chilvers et al. 2003). Following fishery closure, dolphins restructured and homogenised their network, suggesting that structuring was fishery-induced (Ansmann et al. 2012). Our study shows similarities, but also important differences. First, in the population studied by Ansmann et al. (2012), dolphins fed on discards, while our dolphins followed operating trawlers, presumably feeding actively inside/behind the net (Genov et al. 2008; Kotnjek 2016). Second, structuring in our study related to temporal rather than spatial segregation, and did not appear only fishery-related. Another study in the Mediterranean Sea related dolphin association patterns to bottom trawling and fish farming, but animals mixed more frequently than ours (Pace et al. 2012).

Human activities can likely alter behaviour and social structure of mammals (Rutledge et al. 2010; Ansmann et al. 2012) and this may well be the case here. However, causal links are unclear and it is difficult to ascertain what came first. The inherent social structure itself, and social learning, may lead to differential behaviour and interactions with anthropogenic activities, without these activities changing the social system in the first place. It is interesting to note that the pair trawler fishery in our area closed in 2012. This did not appear to change associations or temporal patterns, but cluster A did appear to increase rates of interactions with bottom trawlers (Morigenos, unpublished data).

Diet information for this population is limited, but dietary preferences may explain different fishery-related foraging tactics. Both clusters were observed taking mullets (Mugil/Liza sp., Genov et al. 2008, Morigenos, unpublished data) and both regularly feed in the core study area. Their diets, therefore, overlap, but to an unknown extent. However, the apparent ‘switch’ of cluster A to bottom trawlers after the closure of pair trawler fishery suggests that behavioural specialisation and hunting techniques, rather than prey preference, may be more likely. Our further research aims to provide better insight into the feeding ecology of this population through stable isotope analysis.

Whether interactions with trawlers increase fitness (by maximising energetic intake and minimising expenditure) or decrease it (through increased bycatch), is unknown. Both clusters produce new offspring and appear stable, and there is no evidence of trawler-related bycatch in this area.