Introduction

The diversity and plasticity of social behavior within a species have been shaped over evolutionary timescales by the interaction of ecological, phylogenetic, developmental, and genetic factors (Kappeler et al. 2013). Some species have sufficient behavioral plasticity to allow adaptation to challenges occurring over shorter timescales (Ghalambor et al. 2007; Laland and O’Brien 2012). For example, anthropogenic activities can rapidly change ecological conditions, which some species have initially responded to through learning and behavioral plasticity (Tregenza 1995). If these activities cause a food interaction with the animals, they are described as “food-conditioned animals” because they have acquired food-based behaviors through operant conditioning (Samuels and Bejder 2004; Breck et al. 2008; Finn et al. 2008; Mazur and Seher 2008). This learning process arises from repeated exposure to human stimuli, behavioral response to those stimuli, and reinforcement of behavioral responses because of food reward (Whittaker and Knight 1998). This learned information may be acquired individually or socially (Sargeant and Mann 2009). Social acquisition is potentially an important source of behavioral uniformity within groups and variation in behavior and sociality between groups (Laland and Galef 2009). Therefore, it is likely that behavioral plasticity continuously shapes the individual’s phenotype, including sociality across that individual’s lifetime (Whiten and van Schaik 2007). Altered social structuring may in turn influence the transmission of novel behaviors among social groups by social learning, thereby ratcheting up intergroup variation in phenotype. Here, we investigate this behavioral learning in a highly social marine mammal, the killer whale (Orcinus orca).

Killer whales are emerging as a key species for the study of ecological influences on sociality (Bigg et al. 1990; Baird and Whitehead 2000; Parsons et al. 2009; Beck et al. 2012; Foster et al. 2012). Social structure in killer whales has been widely described and investigated and is typically characterized by strong natal philopatry and stable hierarchically structured social units (e.g., Bigg et al. 1990). However, a number of studies have described variation in sociality between and within populations of killer whales (e.g., Baird and Whitehead 2000). Indeed, a recent study on killer whales in the Northeast Atlantic suggested that sociality is plastic and can be modified to adapt to local ecological conditions (Beck et al. 2012). Moreover, in the Northeast Pacific, group size of killer whales was related with prey abundance, where smaller groups were seen when prey availability was low and that seem to take place 2 years after a lower phase of the Pacific Decadal Oscillation (Lusseau et al. 2004). Anthropogenic activities have also been proposed to influence the social structure and behavior of killer whales; as an example, it has been suggested that targeted removals of individuals for dolphinariums could have altered the social structure into isolated groups in northeastern Pacific populations (Williams and Lusseau 2006). The same populations are now facing social fragmentation related to low abundance of their main prey, Chinook salmon (Oncorhynchus tshawytscha) (Parsons et al. 2009; Foster et al. 2012). Variation within populations can therefore be mediated by anthropogenic activity and changes to local ecological conditions. The transmission of information within this same killer whale population has been observed to occur from the matriarch down through the matriline (Brent et al. 2015). In this study, we quantify measures of sociality in killer whales in the Strait of Gibraltar and investigate changes in these metrics during the emergence of a novel behavior.

Killer whales occur in the Strait of Gibraltar throughout the year, with sightings peaking between April and September (Horozco 1598; Casinos and Vericad 1976; Guinet et al. 2007; de Stephanis et al. 2008; Foote et al. 2011; Esteban et al. 2013). This peak presence coincides with the migration of their main prey, the Atlantic bluefin tuna (Thunnus thynnus) (hereafter ABFT). ABFT enter the Mediterranean Sea from the Atlantic Ocean in late spring to breed (Cetti 1777; Sella 1929; Rodríguez-Roda 1964). They return from their breeding areas in the late summer (Lozano 1958; Aloncle 1964; Rodríguez-Roda 1964). Until 2011, East Atlantic Stock of ABFT was clearly declining due to high fishing pressure (ICCAT 2011; Taylor et al. 2011). Killer whales in the Strait of Gibraltar have been observed feeding using two distinct foraging behaviors. The first of these behaviors is the endurance-exhaustion active hunting technique, where the whales chase free-ranging tuna together in a cooperative behavior (Guinet et al. 2007; Esteban et al. 2013). The second behavior is direct depredation from the ABFT dropline fishing hooks (de Stephanis et al. 2008; Esteban et al. 2013). Killer whales patrol the dropline boats area until they find a fish hooked on a line and depredate it before fishermen can bring the tuna to the surface. This second foraging method appears to be non-cooperative and done on an individual basis (RE, personal observation). The Spanish (around 45 boats, 12 m long) and Moroccan (around 200 boats, 6 m long) dropline tuna fishery started operating in the Strait of Gibraltar in 1994 (Srour 1994; de la Serna et al. 2004), only during summer (July–August). Killer whales have been interacting with this fishery since at least 1999, and their distribution is clearly related with the presence of dropline boats in the area. The Moroccan fleet is located east of the Kamara Ridge, and Spanish fleet is placed between Monte Seco and Monte Tartesos (de Stephanis et al. 2008). Due to the seasonality of the dropline fishery, interactions between killer whales and the fishery occur only in summer (de Stephanis et al. 2008; Esteban et al. 2013), whereas active hunting has been observed during spring (Guinet et al. 2007) and summer (Esteban et al. 2013). The aim of this study is to address whether the involvement of some pods in this new foraging behavior that requires less coordinated behavior leads to more homogenous social structure.

Materials and methods

Data collection

Study area

Data collection was conducted from 1999 to 2011 in the Strait of Gibraltar (Fig. 1). These waters lie between 005° 00′ and 006° 30′ longitude west and are bounded to the north by the Iberian Peninsula and to the south by Africa. The bathymetry of the strait is characterized by a deep canyon with an east to west orientation, with shallower waters (200–300 m) on the Atlantic side and deeper waters (800–1,000 m) on the Mediterranean side. It was not possible to record data blind because our study involved the collection of data from focal animals in the field over a prolonged period and required a certain level of expertise and therefore that data collection be undertaken by the lead investigator (Taniello and Bakker 2015).

Fig. 1
figure 1

Whale tracks map during spring time in the shallow waters of the Gulf of Cadiz (in gray) and in summer time in the central waters of the Strait of Gibraltar (in black)

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Sampling protocol

Field surveys were conducted from whale watching operators (Firmm, Whale Watch España, and Aventura Marina) during summer 1999 and 2000 and from CIRCE’s research motorboat (mv Elsa) between 2001 and 2011, surveying the Strait of Gibraltar throughout the whole year. Transects were conducted without any predefined track for each of these surveys, but they covered the whole bathymetric range throughout the entire year, crossing the isobaths as perpendicularly as possible. Killer whale and dropline fishing boat presence was assessed according to the protocol of de Stephanis et al. (2008).

It is well established that individual killer whales can be recognized by unique natural marks, scars, and pigmentation patterns using photo-identification (e.g., Bigg 1987). Whenever killer whales were encountered during the surveys, photographs of their dorsal fin, saddle and eye patches were taken from both sides of the animals. Identified whales were given a unique alpha-numeric code. Matches with previously identified individuals were made by comparing each new photograph with all the others in the photo-identification catalogue, managed and maintained by CIRCE and available online at www.cetidmed.com. Photographs were quality ranked, based on angle, focus and fin exposure in bad (Q0), good (Q1) and excellent quality (Q2: perfectly focused photograph, in a perpendicular angle with entire fin exposed). Only Q1 and Q2 photographs were used to reduce the risk of misidentification. Animals that could not be matched but were positively identified on more than two Q2 photographs were given a new identification code and were added as new individuals in the catalogue.

Using photographs combined with direct observations, we determined sex and age category for all whales. Following the definition of sexual maturity described by Olesiuk et al. (1990), individuals were categorized into different age classes as (i) calves were individuals younger than 1 year, (ii) juveniles were defined as individuals aged from 1 year old until they reach a mature size, (iii) adult females were mature size individuals, seen with a calf in echelon position, or with a mature size during the whole study period without developing the dorsal fin, and (iv) adult males were individuals with a “sprouted” dorsal fin.

An animal was assumed dead whenever it was not encountered and photographed with its family group (stable groups where individuals are socially cohesive (Bigg et al. 1990)), on more than two consecutive years, or if it was found stranded.

Analysis of social structure

Whales were classified in two foraging classes as either (i) observed interacting with the ABFT dropline fishery on at least one occasion (hereafter INT) or (ii) never observed interacting with the ABFT fishery and only observed actively hunting using the endurance-exhaustion technique (Guinet et al. 2007) (hereafter NOT). While some individuals switched between foraging behaviors and have been observed both actively hunting and interacting with the fishery, other individuals were exclusively seen chasing tuna actively.

Individuals were defined as associated with others based on group membership, which was defined as animals within ten body lengths of one another engaged in similar and/or coordinated behavior (Williams and Lusseau 2006). We feel that this definition is adequate for discriminating between groups and feeding aggregations, which is important given that most of our observations were during foraging. The validity of this is supported by our findings of highly stable group structure over a 12-year period including known mother-offspring pairs, which is a pattern consistent with the previous descriptions of killer whale association patterns in other populations (e.g., Bigg et al. 1990) but is not consistent with a random aggregation due to prey concentrations (Whitehead 2008). Individuals photographed in the same group at least once during a day were considered associated for the day (i.e., the sampling period) (Whitehead 2008). Calves and individuals that died during the study period were excluded from the analysis to avoid bias. In order to minimize the effects of sample size and rarely encountered individuals, the analysis was restricted to animals sighted on at least four different days. Only one group had been seen on 4 days, and all other individuals for the rest of the groups were identified on 6 to 72 different days (see Tables 1 and 4 in Online Resource 1). Removing the individuals only observed on 4 days did not change the significance of any of the results reported here.

The strength of association between dyads (pair of individuals) was calculated using the half-weight association index (HWI) to maximize the comparability with other studies and because it can be more suitable than other indices, such as the simple ratio index, when not all individuals within each group have been identified (Cairns and Schwager 1987; Ginsberg and Young 1992). The HWI measures the proportion of time individuals were seen together in groups and ranges from 0 (two individuals never seen together) to 1 (never seen apart). To be consistent with the literature on killer whale social structure, we classified social groups as “pods” (see Bigg et al. 1990). Pods were defined as a group of individuals that show clear preferences for association with certain other individuals, spending over 50 % of their time together (>0.5 HWI) over a period of years, and some of these associations are extremely strong, with individuals virtually spending all of their time together (Bigg et al. 1990). We calculated the social differentiation (S), as the coefficient of variation of the true association indices among pairs of individuals in terms of the time actually spent together. Social differentiation less than about 0.3 indicates rather homogenous societies, S greater than about 0.5 well-differentiated societies, and S greater than about 2 extremely differentiated societies (Whitehead 2008). Moreover, we also calculated the correlation between true and estimated association indices (r) to test if our data accurately described the social structure of the whales. This index is an indicator of the power of the analysis to detect the true social system (1 indicates a perfect analysis and 0 an unreliable one) (Whitehead 2008).

The lagged association rate (LAR; Whitehead 1995) was used to model temporal trends in association. LAR is an estimate of the probability that if two individuals are associated at τ 0; they will also be associated at τ 1, τ 2, τ 3, etc. Standard errors were estimated by jackknife methods (Efron and Gong 1983). All of the aforementioned parameters were calculated in SOCPROG 2.4 (Whitehead 2009).

A weighted social network was defined by the HWI matrix showing individuals (nodes) connected by their HWI (edges), using the Kamada-Kawai layout (Kamada and Kawai 1989) to visualize the network diagram using the statnet package (Handcock et al. 2003). We evaluated network structure using the following: social network metrics, strength which is the sum of weighted ties for a given individual (Barthélemy et al. 2005), and betweenness is a measure of how often an individual falls on the shortest path connecting other individuals in the network (Freeman 1977). Secondly, we used a modularity matrix technique, controlling gregariousness of individuals (Whitehead 2008), which quantifies the tendency of nodes to cluster into cohesive subgraphs and identifies the most parsimonious network division (Newman (2004) considered appropriate a modularity >0.3).

Then, we compared the social network metrics and HWI between and within the two foraging classes (INT and NOT) to test whether the emerging modular structure was related to the foraging behavior, using the i-graph package (Csardi and Nepusz 2006). Their significance was evaluated by performing permutation tests on the observation stream rather than on the association matrix using the asnipe package (Farine 2013) to avoid sampling biases (Bejder et al. 1998; Whitehead 2008; Sundaresan et al. 2009; Croft et al. 2011). The model randomizes the data stream while restricting swaps between and within foraging classes. Randomizations were repeated 1,000 times, and the p values were calculated by comparing the observed statistic test calculated for each permutation.

Multiple regression quadratic assignment procedures (MRQAP) by the Double Semi-Partialling or DSP (Dekker et al. 2007) were used with the asnipe package (Farine 2013) to test whether similarity in maternal kinship, foraging class, and sex-age class were significant predictors of association. MRQAP regression is a type of Mantel test that allows for a response matrix to be regressed against multiple explanatory matrices that represent dyadic attribute relationships. The response matrix in this procedure contained observed association strengths (HWI) in the social network, while foraging classes, sex-age classes, and maternal kinship matrices served as explanatory matrices. Sex-age classes and foraging classes (INT or NOT) matrices were created by giving a value of 1 to two whales belonging to the same category and 0 otherwise. Maternal kinship matrix was created giving a value of 1 to two individuals identified as mother and calf and 0 otherwise.

The aforementioned packages were run with the open-source statistical programming language R 3.2.2 (R Core Team 2014).

Results

A total of 20,617 photographs were analyzed, representing 24,436 individuals (a given photograph can include several individuals). We sighted a total of 130 groups in 101 days during more than 4,000 h of effort from 1999 to 2011 (Table 1 and Fig. 1). Most of the sightings were made in summer with 86 sampling days against 15 sampling days in spring. A consistent high presence of dropline fishing vessels was sampled in a 1-km radius of killer whale during summer time (mean ± SE = 51.8 ± 46.44). During summer time, in 90 % of sampling days, there were fishing vessels in the whales’ vicinity. In 99 % of the sampling days that killer whales were surrounded by fishing vessels, they were seen interacting with their gear. Both in spring and summer, killer whales were mostly observed foraging, only in 7 % of the encounters they were doing other behaviors (resting, socializing, or travelling). Forty-seven killer whales were identified within the study area. After applying the restrictions needed for a quality study of their social structure and in line with previously published studies (e.g., a minimum of four sighting days, no deaths or calves; Ottensmeyer and Whitehead 2003; de Stephanis et al. 2008; Tosh et al. 2008; Beck et al. 2012), 28 individuals were used in the analyses, of which 14 animals were seen interacting with the dropline fishery and 14 animals were never seen interacting, giving us a balanced sample size of the relations between and within individuals with different or same foraging behavior (Table 4 in Online Resource 1). All 28 individuals were seen in spring actively hunting, 18 of these were also seen in summer, of which 14 have been seen interacting at least once with the tuna dropline fishery.

Table 1 Summary of the sampling effort of the long-term association study of killer whales, in the Strait of Gibraltar
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Quantification of social differentiation revealed that the social structure of killer whales was highly differentiated (S ± SE = 1.93 ± 0.09, N = 28). Correlation between true and estimated association indices indicated good representation of the real social systems (r ± SE = 0.97 ± 0.01, N = 28). Group sizes of killer whales encountered interacting with ABFT fishery ranged from 1 to 12 individuals (mean ± SE = 7.83 ± 1.56), which was the same when whales were observed hunting actively (range 1–12, mean ± SE = 6.20 ± 2.38). The results of the temporal analyses (Fig. 2) suggest that INT-NOT associations decrease with time, while INT-INT and NOT-NOT associations are quite stable with time over the 12 years of study.

Fig. 2
figure 2

Plots of lagged association rate for a number of different types of associations: within all individuals (ALL-ALL), within INT individuals (INT-INT), within NOT individuals (NOT-NOT), and between INT and NOT (INT-NOT). A different moving average was chosen for each type of association to smooth lines. Jackknife standard error bars are shown on the three lines. Null indicates the expected LAR of all individuals if individuals had a random chance of associating

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Permutation tests revealed that INT whales had higher HWI, strength, and betweenness than NOT individuals (Table 2). INT individuals had higher HWI and strength than expected from the randomization data, but it was not the case for betweenness. For NOT animals, the values for HWI, strength, and betweenness were higher than expected from randomized data (Table 2).

Table 2 Social network metric comparison with randomized null data set
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The social network was divided into five pods (>0.5 HWI for all individuals within each pod) by a modularity estimated as 0.50 (Fig. 3). All individuals remained in the pod where they were found for the first time; thus, no dispersion between pods has been observed over 12 years. This reinforces the relationship between association pattern and foraging classes. All individuals from A1 and A2 pods have been seen every year in summer both interacting with dropline fisheries and actively hunting; therefore, they all belonged to the INT foraging class. They were also seen actively hunting in spring in two different years (2005 and 2010). In contrast, pods B, C, and D only included NOT whales, i.e., whales that were never observed depredating fishing lines. Pods C and D have only been observed in spring actively hunting in the Gulf of Cadiz and were therefore unlikely ever exposed to the ABFT dropline fishery. Pod C was seen on 25 different days in three different years (2002, 2010, and 2011), while pod D was only observed four different days in two consecutive years (2003 and 2004), but each time all individuals remained together in their own pod. On the other hand, pod B has been observed on 27 different days in three different years (2006, 2008, and 2011), both in spring in the Gulf of Cadiz and in summer in the central waters of the Strait of Gibraltar (24 different days), and only actively hunting in both locations (although they were seen surrounded by dropline fishing vessels in summer, no interaction was ever observed) (see Table 1).

Fig. 3
figure 3

Social network of killer whales in the Strait of Gibraltar separated in five different pods as defined by modularity. Squares indicate INT individuals and circles NOT individuals. Black edges are within-pod weighted HWI between individuals (>0.5 HWI), and gray edges are between-pod weighted HWI between individuals

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Individuals belonging to A1 and A2 in 2011 formed a single pod A at the beginning of the study with 11 individuals in 1999. Between 1999 and 2005, this pod saw the birth of five viable calves and the death of one juvenile, increasing the number up to 15 individuals in 2005. In 2006, pod A split into A1 and A2; however, strong associations are still present between these two pods (Fig. 3). Therefore, we have observed a transformation in the social structure of INT whales.

The MRQAP analysis showed a significant effect of maternal kinship and foraging class but not of sex-age class on the observed HWI of all connected dyads (Table 3), suggesting a matrilineal social structure with pods defined by different foraging classes. All these measurements were replicated, including individuals seen on more than five occasions only in the analyses (i.e., pod D was excluded from the analyses), but this did not affect the significance of the results (available in Online Resource 2).

Table 3 MRQAP regression model
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Discussion

Social structure of killer whales in the Strait of Gibraltar

In this study, we found that killer whales in the Strait of Gibraltar do not associate randomly with one another but rather tend to associate with specific individuals. Associations between individuals within a pod are both strong and enduring. This kind of structure can produce a disconnected network composed of strongly connected components representing isolated social units (Whitehead 2008). In the Strait of Gibraltar, we found an extremely differentiated social structure composed of fie different pods, divided into two components that have never been seen in association with each other. Killer whales in the Strait of Gibraltar are organized in identifiable pods, reflecting results from studies of killer whales in the North Pacific (Heimlich-Boran 1986; Bigg et al. 1990; Matkin et al. 1999; Ivkovich et al. 2010), Northeast Atlantic (Similä 1997; Beck et al. 2012), and Marion Island (Tosh et al. 2008), i.e., killer whales have a relatively stable, matrilineal social structure, which may be an inherent trait in this species.

Plots of lagged association rates show that most associations are stable over the duration of the 12-year study period. However, the probability of an INT individual being associated with a NOT individual decreased with time and fell in less than 1 year to a level expected if individuals were associating randomly. This could indicate that individuals belonging to different foraging classes rarely associate with each other and have weak social bonds. As all the animals from the same pod belong to the same foraging class, this could also be a product of the inherent social structure of killer whales, with strong relations between relatives, and accordingly individuals tend to interact with individuals within their pod.

Evidence for plasticity in sociality and foraging behavior

Assessment of Atlantic Bluefin tuna indicated that spawning stock biomass peaked over 300,000 t in the late 1950s and early 1970s, then declined to about 150,000 t until the mid 2000s and stabilized around total allowable catch levels established by the International Commission for the Conservation of Atlantic Tunas (ICCAT) at the end of the 2010s. Catches of ABFT were seriously under-reported from 1990s through 2007 causing the decline of the stock over that period (ICCAT 2014a). Environmental changes such as the decline of ABFT (Taylor et al. 2011; ICCAT 2014b) and the establishment of a new fishery (Srour 1994; de la Serna et al. 2004) have arisen in the Strait of Gibraltar. In parallel, changes were observed in killer whale social structure. Specifically, pod A split into two (A1 and A2) pods. Parsons et al. (2009) found a dynamic social structure in killer whales of the Pacific Ocean, with significant changes in the strength of associations between years related to changes in population dynamics. Large groups may undergo a feeding disadvantage and therefore split (Whitehead and Weilgart 2000), and the fission of pods in the wild has previously been observed (Ford et al. 1994; Harms 1997; Baird and Whitehead 2000; Scheel et al. 2001); in fact, Ford et al. (1994) suggested that the death of the oldest female within a pod can cause the fission of the pod. In the Strait of Gibraltar, no old female has died during our study period within pod A; therefore, the fission inferred to have occurred between 2005 and 2006 could be due to the recruitment of new individuals (through the birth of five viable offspring) into the pod. This split occurred only in the pods that belonged to the INT class and therefore depredated the fishing lines. Although it is not clear if this was a causal factor in this change in sociality, the new foraging behavior could have influenced INT social structure in two ways. First, the probable energetic gain provided by the interaction could have supported the recruitment of new individuals in pod A, leading to an increase of pod size. Secondly, the new foraging strategy might require a smaller pod size, causing the split into A1 and A2 to engage in this behavior successfully.

In some cases, human activities can determine social plasticity within the population, segregating animals that use different foraging strategies, e.g., bottlenose dolphins (Tursiops sp.) have coupled their social structure to a foraging cooperation with artisanal fishery in Brazil (Daura-Jorge et al. 2012). Moreover, social plasticity has been previously described for killer whales in the Atlantic Ocean, which appear to modify sociality due to variation in local and seasonal ecological conditions (Beck et al. 2012). Our results suggest plasticity in both sociality and foraging behavior. First, all individuals were observed actively chasing tuna, but only two pods were seen interacting with the dropline fishery, suggesting the transmission of this behavior may have been restricted by social structure. Second, the differences in sociality between the two foraging classes were evidenced by the network metrics. Associations were higher within than between foraging classes, indicating stronger intra-class relationships. As a result, the social network clustered according to foraging classes and maternal kinship suggesting transmission of foraging behavior occurs mainly within a matrilineal social pod, suggesting horizontal (between individuals of a pod) and vertical transmission (from mothers or other adults to offspring), at least within A1 and A2 pods. Presumably, interaction is less energetically costly than the endurance-exhaustion hunting technique, and we hypothesize that this may relax the ecological constraints that select for stable social groups during more coordinated active foraging behavior. Associations between INT killer whales showed higher strength and mean HWI than expected, resulting in a more compact social structure than NOT killer whales. However, betweenness, which quantifies how much of a bottleneck is created by an individual (Lusseau and Newman 2004), did not differ. This could be due to the recent split of pod A, as all individuals from A1 are still connected with A2, creating a very homogenous structure where no individual is more important than another. Individuals from A1 and A2 exhibit a non-coordinated behavior (RE, personal observation) when interacting with the fishery. Felleman et al. (1998) suggested that when prey are scarce or patchily distributed, killer whales have to spend more time locating prey and also more time spread out, away from the rest of the pod. The connectivity of a killer whale network can thus change in response to food availability, becoming more connected whenever prey abundance is high (Foster et al. 2012). Deep dives were registered in ABFT while crossing the Strait of Gibraltar in summer (Wilson and Block 2009), but dropline fisheries bring these ABFT to the surface (Srour 1994; de la Serna and de Urbina 2010), where killer whales depredate them, so dropline fisheries are increasing the availability and concentration of prey, and this could be affecting connectivity within A1 and A2 pods. Conversely, when whales are actively hunting (all five pods), ABFT are not artificially concentrated, so that association among pods is lessened as each pod spreads out to hunt, but individuals within pods exhibit a tight association during this coordinated behavior (Guinet et al. 2007), resulting in a more differentiated structure among pods within this population. Additionally, smaller killer whale groups in the Pacific Ocean have been sighted whenever the abundance of their main prey is low (Lusseau et al. 2004). Further research in the area should be focused on the relation between ABFT density in the area and killer whales’ social structure, through CPUE of trap nets, an ancient passive fishing gear that has been operating in the area for centuries (Ravier and Fromentin 2001).

Interaction of social structure and social learning on foraging strategy

In other studies where social species have been observed using anthropogenic food sources, changes in behavior have been argued to be a result of social learning such as bottlenose dolphins feeding on trawler discards (Chilvers and Corkeron 2001; Ansmann et al. 2012); killer whales and sperm whales (Physeter macrocephalus) depredating fish from long lines (Whitehead and Rendell 2004; Poncelet et al. 2010; Guinet et al. 2014; Tixier et al. 2014); chimpanzees (Pan troglodytes) raiding crops (McGrew 2004; Hockings et al. 2012), African elephants (Loxodonta africana) (Chiyo et al. 2012) and Asian elephants (Elephas maximus) (Zhang and Wang 2003); and bears (Ursus sp.) eating human’s discarded food and garbage (McCarthy and Seavoy 1994). Social learning has been widely reported in vertebrate species as an important process for acquiring information about a fluctuating environment (Borenstein et al. 2008; van der Post and Hogeweg 2009; Whiten et al. 2011). Additionally, exposure to parental care has been related with social learning in canids (Nel 1999) and cats (Schaller 1972; Caro 1994). Offspring benefit from the knowledge of more experienced individuals. There is a dependence on vertical transmission of information, when environmental challenges are experienced by different generations, whereas horizontal transmission is favored when environmental change is faster than generation time (Fragaszy et al. 2003). Consequently, horizontal transmission may allow the rapid spread of innovations and improve the efficiency of individuals foraging on temporary food patches or during periods of rapid environmental changes, while vertical transmission may promote the development of fundamental skills (Thornton and Clutton-Brock 2011). In this study, foraging strategies are different between pods, but all members from each pod participate in the same strategies. Although often difficult to distinguish, such patterns could arise through a combination of vertical transmission within matrilineal pods or by vertical and horizontal transmission with individuals aligning their behavior with that of other pod members (Boyd and Richerson 1985). This within-pod uniformity and between-pod differences in a recently derived behavior is consistent with (but not proof of) selective transmission via social learning (Galef 1992). In elephants, the possession of improved discriminatory abilities by the oldest individual in a group was demonstrated to influence the social knowledge of the complete group (McComb et al. 2001). The transfer of knowledge within a matrilineal pod and in particular from the matriarch down to her offspring through social learning and interaction has been suggested as a way of both promoting stable foraging traditions (Riesch et al. 2012) and buffering kin fitness during times of ecological change and low prey abundance in killer whales (Brent et al. 2015). Therefore, we might be describing the creation of a novel foraging tradition in some killer whales of the Strait of Gibraltar, probably through vertical and horizontal transmission. Although the new fishery has been operating since 1994, our study started in 1999. At that time, all the whales from pod A (and posteriorly A1 and A2) were already interacting with the fishery, and no information is available from previous years, so we could not study the spread of this foraging behavior from its commencement. However, it is interesting to highlight that members that belonged initially to pod A carry on the behavior in their new pod, years after the split into A1 and A2. Only members of pod A (and its resulting fission A1 and A2 pods) have ever been observed depredating fishing lines, even when another pod has been observed in the vicinity of fishing boats. There has been no spatial or temporal overlap between dropline fisheries and pods D and C since 1999, but pod B has been seen in the vicinity of dropline boats in summer 2006 and 2007, and interacting behavior was not observed for any individual of pod B. This suggests the influence of social structure on learning this particular foraging technique. Future research efforts should be promoted to monitor killer whales to determine if pods C or D start visiting the dropline fishing area in the next years and if any of the NOT whales start to interact with the vessels, resulting in the spread of this foraging technique through part or the whole population.

Living in social groups with clear matrilineal social structure favors the evolution of social learning, leading to the idea that social learning is an adaptation for social living (Klopfer 1961; Templeton et al. 1999). For example, among vervet monkeys (Chlorocebus pygerythrus), affiliative social interactions, such as grooming, mutual tolerance at feeding sites, and formation of aggressive alliances, occur within matrilines (Seyfarth 1980; Cheney and Seyfarth 1990). In spotted hyenas (Crocuta crocuta), rank is acquired through defensive maternal interventions and coalitionary support (Engh et al. 2000). Elephants can coordinate their movements with individuals genetically related, indicating matrilineal dominance (Charif et al. 2005).

In elephants, socially transmitted knowledge may result in higher per capita reproductive success for groups led by older individuals (McComb et al. 2001). Interactions between animals and anthropogenic activities could also change life history parameters as in the Yellowstone National Park, where some grizzly bears, Ursus arctos, quickly learnt to exploit visitor’s food and have better life history parameters (calving interval, age of maturity, and birth rates) than those bears that did not (Craighead et al. 1995). Interacting with the dropline fishery could be considered as advantageous social knowledge within these killer whales, as it would require less energy than chasing individual tuna for 30 min at high speed (Guinet et al. 2007). The probable effect of the depredation ability on the reproductive success of INT individuals will be studied in the future.

In wild populations, it is difficult to rule out genetic inheritance of behavior socially learned and equally difficult to ascertain the role of genetic and environmental variation upon observed differences in behavior (Laland and Galef 2009). A genetic study, which included samples from Strait of Gibraltar killer whale pods (Foote et al. 2011), assigned all individuals to a single population based on nuclear DNA loci. However, some genetic differences have been found at the pod level: individuals belonging to A1, A2, B, and C pods shared the same mitochondrial DNA (mtDNA) control region haplotype (Atl_1_33), while D pod had mtDNA control region haplotype Atl_1_29 (Foote et al. 2011). We cannot therefore exclude a role for genetic differences, however unlikely, as a factor constraining behavior (see arguments proposed by Laland and Janik 2006 on how variation in mitochondrial DNA might influence the transmission of a particular behavior). Similarly, as some pods (C and D) have only been observed in spring, it may be that ecological variation among pods has resulted in behavioral variation. However, given that B pod has never been seen interacting with the dropline fishery but has been observed in summer time in the same waters where their closely related A1 and A2 pods interact, then a role for social structure constraining social learning seems at least equally as parsimonious as genetic or ecological factors driving inter-pod variation on foraging behavior.

Summary

Our study illustrates the influence of ecology on social structure, by contrasting patterns of association in a highly social mammal between ecological contexts. Our study provides an insight into how the highly cohesive matrilineal social structure of killer whales and their ability to innovate new foraging techniques can interact with one another and result in pod-specific foraging behaviors. Such observations at the earliest stages of diversification in foraging behavior and social segregation may provide insights into the processes that ultimately result in the formation of socially isolated discrete ecotypes in killer whales and potentially more broadly among highly social species in which social learning plays a role in generating behavioral variation.