# The role of social transmission in the use of a new behaviour by killer whales in response to fisheries

Erwan Auguin, Christophe Guinet, Johann Mourier, Eric E.G. Clua, Nicolas

Gasco, Paul Tixier

► To cite this version:

Erwan Auguin, Christophe Guinet, Johann Mourier, Eric E.G. Clua, Nicolas Gasco, et al.. The role of social transmission in the use of a new behaviour by killer whales in response to fisheries. Animal Behaviour, 2025, 225, pp.123228. 10.1016/j.anbehav.2025.123228. hal-05194842

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# The role of social transmission in the use of a new behaviour by killer whales in response to fisheries

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Erwan Auguin a, b, *, Christophe Guinet b, Johann Mourier a ID, Eric E. G. Clua c, d ID,
Nicolas Gasco e, Paul Tixier a

## ARTICLE INFO

**Article history:**

Received 21 November 2024

Initial acceptance 13 January 2025

Final acceptance 19 March 2025

Available online 31 May 2025

MS. number 24-00699R

**Keywords:**

behaviour heterogeneity

depredation

ecosystem management

human–wildlife conflict

killer whale

learning pathway

marine top predator

Orcinus orca

social transmission

Human exploitation profoundly impacts ecosystems but can also provide novel feeding opportunities to which animals may respond by learning new foraging behaviours. Among these, feeding on fisheries catch or byproduct, a behaviour termed 'depredation', has been observed in many large marine predator species globally. However, for social species, how social transmission influences both the acquisition of and the level of engagement in such behaviour by individuals within populations remains poorly understood. In this study, we analysed 18 years (2005–2022) of photo-identification data, with a combination of both social and diffusion models, to assess the role of social transmission in the acquisition of depredation by social units of the killer whale population of the Crozet Islands, as well as the extent to which social units engaged in this behaviour once acquired. We found that an increasing number of social units acquired depredation behaviour over time and that they did so through learning from other units and independent learning. We also demonstrate that during the years following this acquisition, their level of engagement in depredation was influenced by social preferences, with closely associated units showing similar levels of depredation and certain units acting as structural key nodes facilitating the transmission of depredation within the network. However, past the year when the entire population had acquired depredation, we found that factors other than social transmission came into play to explain heterogeneity in depredation levels observed across social units. Together, these findings highlight the role of social dynamics in shaping species' response to human-induced changes in their environment and in leading to intrapopulation behavioural heterogeneity that can have major implications for the long-term conservation of top marine predator populations.

© 2025 The Author(s). Published by Elsevier Ltd on behalf of The Association for the Study of Animal Behaviour. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Behavioural shifts are one of the main responses of species to changes in their environment by human activities (Sih et al., 2011; Tuomainen & Candolin, 2011). While these responses can determine the ability of species to adapt, they can vary greatly across conspecifics within populations and generate heterogeneity in behaviours such as foraging, movements or communication between individuals or groups (Jolles et al., 2020; Planas-Sitjá et al., 2015). When it leads to variable fitness across individuals or groups, such heterogeneity in response to anthropogenic impacts can be a key determinant of population dynamics, species

evolutionary trajectories and ecosystem functioning (Bolnick et al., 2011; Vindenes & Langangen, 2015).

Intrapopulation behavioural heterogeneity can be driven by multiple factors such as competition, personality traits, habitat differences, resource availability and/or social environment (Araújo et al., 2011; Bolnick et al., 2003; Dall et al., 2012; Montiglio et al., 2013; Svanbäck & Bolnick, 2006). Typically, in social species, individuals often use information from conspecifics during social interactions when making behavioural decisions (Galef & Giraldeau, 2001), and heterogeneity in the quality and quantity of these interactions can lead to nonuniform transmission of information and behaviours through social networks (Cantor et al., 2021). In particular, preferential associations often determine social transmission,

<footnote>a UMR MARBEC, Université de Montpellier-CNRS-IFREMER-IRD, Sète, France</footnote>

<footnote>b Centre d'Etudes Biologiques de Chizé (CEBC), UMR 7372 CNRS-La Rochelle Université – CNRS, Villiers-en-Bois, France</footnote>

<footnote>c Paris Science et Lettres (PSL), CRIOBE UAR3278, EPHE-CNRS-UPVD, Université de Perpignan, Perpignan, France</footnote>

<footnote>d Laboratoire D'Excellence LabEX CORAIL, CRIOBE, Baie Opunohu, Papetoai, French Polynesia</footnote>

<footnote>e Laboratoire de Biologie des Organismes et Ecosystèmes Aquatiques (BOREA), UMR 8067 – MNHN, CNRS, IRD, SU, UCN, UA, Paris, France</footnote>

<footnote>* Corresponding author.
E-mail address: erwan.auguin@ird.fr (E. Auguin).</footnote>
which is the spread of new behaviours or information via social
learning through observation or interaction with others (W. Hoppitt
& Laland, 2008). Social transmission can occur through horizontal
(between individuals of the same generation), oblique (across gen-
erations excluding parents) and vertical (from parent to offspring)
pathways (e.g. McComb et al., 2001; van de Waal et al., 2013; Wild
et al., 2019); and can greatly contribute to intrapopulation behav-
ioural heterogeneity (Brotcorne et al., 2017; Klump et al., 2021). As
such, understanding how new behaviours developed in response to
human-induced environmental changes are acquired by individuals
or groups and spread within populations is key to assessing their
ecological consequences. By influencing activity, energy use, move-
ment, social structure and life-history traits, these new behaviours,
and the extent to which individuals or groups engage in it, ultimately
shape indeed the demographic trajectories of populations
(Fehlmann et al., 2021; Whitehead et al., 2023).

Depredation, which is defined as species feeding on resources raised or caught by humans, is among the new behaviours developed by large marine predators, particularly sharks and marine mammals, in response to the global expansion of fisheries and the new feeding opportunities they generate through fish or byproduct caught on fishing gear (Mitchell et al., 2018; Tixier, Lea, et al., 2021). While marine predators and fisheries interact in many ways that can be positive or neutral in terms of catch, depredation is one type of interaction that can affect the fitness of individuals both positively through access to prey at low foraging effort (Tixier et al., 2015) and negatively through exposure to injury or death from interaction with the gear or retaliation practices from fishers (Lewison et al., 2004; Tixier et al., 2017). This could potentially lead to divergent demographic trajectories within populations if there is a large variation in the extent to which individuals or groups acquire or use depredation as a feeding strategy. However, and despite these contrasted impacts, intrapopulation heterogeneity in depredation on fisheries catches and the pathways through which the behaviour may spread across individuals or groups are poorly understood. Yet, as many of the large marine predators involved in depredation are social species, social transmission is likely to have a major role in the spread of the behaviour by individuals.

The killer whale, *Orcinus orca*, is among the top marine predators most involved in depredation on fisheries catches, with many distinct populations having acquired the behaviour globally (Tixier, Lea, et al., 2021). While this shows the potential of killer whales to rapidly respond to new feeding opportunities in their environment (Belonovich et al., 2021), the species is highly social and social learning is likely to be a key process in the acquisition of the depredation within populations. Killer whales have a social organisation generally including basal groups of closely related individuals (hereafter 'social units') that can be highly stable in time, with a matrilineal structure as documented in multiple populations (Baird & Dill, 1996; Ford, 2019). This social level differs from 'pods', which include multiple genetically related social units that share a close common ancestor. Horizontal, oblique and vertical cultural transmission of behaviours such as acoustic communication and foraging strategies was evidenced within killer whale populations (Deecke et al., 2000; Filatova et al., 2015; Guinet & Bouvier, 1995; Pitman & Durban, 2012), indicating the importance of social dynamics (Riesch et al., 2012). Like these behaviours, one may assume that depredation on fisheries, as a new feeding strategy with low foraging effort, is socially learnt through various transmission pathways (Abramson et al., 2013). These pathways are evident within social units, where all members have been shown to generally share behavioural traits such as vocal dialects (Yurk et al., 2002) and feeding strategies (Guinet & Bouvier, 1995; Pitman & Durban, 2012). However, the role of horizontal social transmission between social units and its contribution to the spread of

depredation in response to human activities, relative to indepen-
dent learning, remains unclear. Yet, this information would help
elucidate the role of social transmission in explaining the large
heterogeneity across social units observed among some pop-
ulations in adopting depredation as a feeding strategy (Auguin
et al., 2024; Cronin et al., 2016).

Around the Crozet Islands (subantarctic islands in the Indian Ocean sector of the Southern Ocean), the long-term photo-identification monitoring of one of the killer whale ecotypes, the so-called 'Crozet killer whales', offers a unique opportunity to investigate the learning pathways of the depredation behaviour. High levels of killer whale depredation have been reported in the region, with over 40% of fishing operations affected. Annually, killer whales remove ~179 tons of Patagonian toothfish, *Dissostichus eleginoides*, caught on the longlines deployed by the commercial fishery operating legally around the islands (Tixier et al., 2020; Tixier, Lea, et al., 2021). The Crozet killer whales were first reported depredating the same year the fishery started in 1996 (Tixier et al., 2020) and underwent a period of additive mortality because of individuals being shot from fishing vessels operating illegally in the area until the early 2000s (Guinet et al., 2015). While the population declined by 60% during this period and has continued declining afterwards (Tixier, Gasco, et al., 2021), the number of individuals involved in depredation increased during the 2000s until all individuals had acquired the behaviour in 2014 (Amelot et al., 2022). However, large variation in the probability of being involved during depredation events was detected among the 17 social units of the population, totalling 87 to 94 individuals in 2020 (Auguin et al., 2024). While illegal fishing has been reduced by surveillance efforts, it still occurs in some areas around Crozet, and killer whale social units most frequently depredating are likely still exposed to lethal retaliation from fishers in these areas (Tixier, Gasco, et al., 2021). For these reasons, understanding the learning pathways underlying the acquisition and the engagement in depredation, a risky behaviour, by social units is essential for assessing its impact on the Crozet killer whale population. More broadly, for a top predator like the killer whale, this knowledge is crucial for understanding how the species may respond to human-induced changes in its environment through behavioural shifts (Fehlmann et al., 2021).

Therefore, in this study, using killer whales of the Crozet Islands as a case study for which individual monitoring data and fishing data were available over an 18 year period (2005–2022), we investigated the extent to which social transmission contributed to the acquisition and the engagement in depredation by social units. Specifically, we aimed (1) to examine the spread of the depredation behaviour across the social units of the population over the study period; (2) to estimate whether social units preferentially associated shared similar levels of engagement in depredation (that is, social assortment); and (3) to assess, using diffusion models at the unit level, the relative contribution of social versus independent learning to the acquisition of the depredation behaviour within the population.

METHODS

Ethical Note

The photo-identification data used for the study were collected from fishing vessels as part of the fishery observers' programme of the Muséum National d'Histoire Naturelle of Paris and the Terres Australes et Antarctiques Francaises (TAAF) and from the coast of the Crozet Islands as part of the Program 109 of the Institut Polaire Français (coordinator: C. Barbraud). All photo-identification protocols were conducted under permits from the TAAF, the French Ministry of Higher Education, Research, and Innovation (APAFIS permit for using animals for scientific purposes #33165-
2021092117135118) and the French National Council for Nature Protection (CNPN) for the IPEV-109 ORNITHOECO project (n°2022-00479-031-001). Long-term monitoring of killer whales in the Crozet Archipelago has been conducted since 1964 using noninvasive photo-identification methods to record individual abundance and spatial distribution. This study focused on 84–97 frequently identified individuals alive in 2020, including 8 calves, 19 juveniles, 6 subadult and 14 adult males, 34 reproductive and 9 postreproductive females (Tixier, Gasco, et al., 2021). The photo-identification protocols were implemented without any capture or direct contact with the animals, and with a passive approach at a distance (animals were never actively approached). Observations were conducted from an opportunistic platform, either a fishing vessel or the shore of Possession Island, which the animals approached voluntarily. At sea, during interactions with killer whales (that is, depredation events), fishery observers took photos when fishing vessels were typically engaged in longline retrieval operations, moving at low speeds (~1 knot) in a unique direction with no additional manoeuvres to approach the whales. From the shore of Possession Island, trained personnel based at the Alfred Faure research station photographed killer whales when individuals foraged on elephant seals and penguins along the shoreline. All photos were taken from a distance of at least 30 m to avoid any interference with their foraging behaviour near rocky shores. All research protocols were carefully designed to minimize disturbance, informed by extensive knowledge of killer whale ecology and the potential impacts of observation methods. The research team led by Dr C. Guinet and Dr P. Tixier has over 20 years of experience in fieldwork on killer whales and works closely with international specialists to ensure best practices and risk minimisation.

## Data Collection

We identified killer whale individuals by distinctive and long-lasting natural markings, particularly on their dorsal fins and sad-dle patches, and we used photographs of these body parts to monitor the population over time (Bigg, 1987). Trained personnel collected photo-identification data of the Crozet killer whales be-tween 2005 and 2022 from two platforms: from the shore of Possession Island (46°S–51°E) when individuals foraged on seals and penguins along the coast (Tixier et al., 2019) and from all licensed fishing vessels of the longline toothfish fishery in the Crozet Exclusive Economic Zone (EEZ, 44–48°S, 45–55°E). Fishery observers were present on these longliners at all times and moni-tored 100% of the fishing operations. They recorded information on the date, time, location, fishing effort and catch, as well as on the occurrence of whale depredation, for all longline sets (a longline set is made of a main line of 3–7 km long, bearing thousands of baited hooks deployed on the seafloor at depths >500 m for hours before being hauled back onto the vessel). These data also included the identity of the vessel. We extracted them from the PECHEKER database of the Muséum National d'Histoire Naturelle (Martin et al., 2021). Fishery observers confirmed the occurrence of killer whale depredation during the hauling of a given longline set (hereafter referred to as a 'depredation event') using a combination of cues visible from the surface (see details in Appendix 1).

Following Auguin et al. (2024), we defined a sighting of killer whales temporally as the time interval between the first and the last photographs of killer whales taken during an event of contin-uous presence of individuals around a given fishing vessel or in a specific area along the shore of Possession Island. We assigned a unique code to each sighting. From fishing vessels, we only used

photographs taken during depredation events (see details in Appendix 2). Along with the date, time and location of sightings, we extracted the ID of the social units present during sightings from the photo-identification data based on the composition of the 17 social units as characterized in Auguin et al. (2024). Considering the stability of Crozet killer whale matrilines (Tixier, Gasco, et al., 2021), we assumed that if at least one individual from a given social unit was photographed, the whole unit was present and also engaged in depredation. In all subsequent analyses, we used the number of photographs taken during sightings as the metric for the photo-graphic effort, which was shown to positively influence the number of killer whale individuals identified during sightings at Crozet (Tixier et al., 2017).

## Acquisition of the Depredation Behaviour by Social Units

We used photo-identification data collected from fishing vessels during depredation events (158 743 useable killer whale photos taken during 1463 sightings between 2005 and 2022) to examine changes over time of three metrics associated with the acquisition of the depredation behaviour at the social unit level, each calculated per year: (1) the cumulative number of social units having acquired the depredation behaviour (CSD); (2) the mean number of social units simultaneously engaging in the same depredation event, around the same vessel at the same time (MSP) and (3) the overall proportion of days killer whale depredation occurred out of all fishing days vessels operated around the Crozet Islands (PDD). We calculated the CSD using the year during which individuals from the 17 social units were first confirmed photographed during a depredation event. To account for the influence of the photographic effort on the probability to photograph individuals, we selected 300 photos randomly each year among all depredation events to detect social units. We calculated the MSP (±SD) from 30 randomly selected depredation events, except for the years 2005 and 2006 (mean calculated from 10 randomly selected depredation events because of insufficient number of killer whale sightings with photographic effort). We calculated the PDD as the ratio between the number of fishing days with at least one killer whale or uncertain depredation events recorded by any of the vessels operating around the Crozet Islands over the total number of fishing days vessels operated in the area, using the PECHEKER database (i.e. 2940 fishing days between 2005 and 2022). We assessed trends over time in MSP and PDD using regression models to test whether an increased number of social units acquiring the depredation behaviour resulted in increased numbers of social units being simultaneously present during depredation events or in an increased proportion of fishing days during which killer whale depredation occurred or both (Appendix 3 for detailed procedures).

## Contribution of Social Transmission to Depredation Behaviour

We assessed the role of social transmission and independent learning in the acquisition and the engagement in depredation behaviour by killer whale social units through two steps: (1) by examining the extent to which social units homogenize their level of engagement in depredation behaviour depending on their preferential associations over time through social assortment (Croft et al., 2009) and (2) by estimating the relative contribution of social transmission and independent learning in the way the depredation has spread across social units of the population through a network-based diffusion analysis (NBDA) conducted at the level of social unit (Hasenjager et al., 2021).
## Behavioural Network Assortment

Assortment in a network describes the tendency of social units to be connected to any other social units that share some characteristics (Croft et al., 2009). We used the social assortment approach to assess the correlation between the similarity in the engagement level in depredation behaviour by social units and their preferential associations over 2-year subperiods between 2005 and 2022 (Croft et al., 2009). We used the relative probability of social units being present during depredation events (RPD) as the engagement level in depredation behaviour. We estimated the RPD from a generalized linear model (GLM; details provided in Appendix 4), with a binomial distribution fitted to the occurrence of social units during depredation events and a logit link function (Bolker et al., 2009; Zuur et al., 2009). The model included social unit ID, month and fishing vessel as fixed effects, with an interaction term between the 2-year subperiods and social unit ID to capture temporal variations in depredation behaviour. We selected the final model using stepwise forward selection based on Akaike's information criterion corrected for small samples (AICc; Bartón, 2023). We assessed model fit with the coefficient of determination (R²) and tested the significance using the ANOVA function after verifying the homogeneity of variances (see Appendix 3 for detailed procedures). For subsequent analyses, we categorized the RPD into four discrete levels to capture trends for depredation levels across social units based on GLM predictions: high (RPD ≥ RPD mean), medium (5% ≤ RPD < RPD mean), low (RPD < 5%) and none (RPD = 0).

We assessed the strength of associations between the 17 killer whale social units from the photo-identification data collected both from the shore and from the fishing vessels, using a total of 188 365 photos taken during 1902 sightings (439 from the shore, 1463 from fishing vessels) made over 1152 days (308 from the shore, 874 from fishing vessels). We considered social units associated if they were photo-identified together during the same sighting ('gambit of the group'; Whitehead & Dufault, 1999), and we calculated the simple ratio index (SRI; Cairns & Schwager, 1987) as the strength of association through the proportion of time two social units spent together (0 for pairs never associated; 1 for pairs always associated). We used the SRI as the most appropriate association index when potential observation errors cannot be accounted for, with the SRI not overestimating associations between individuals (W. J. E. Hoppitt & Farine, 2018). In our case, these potential observation errors were associations that we could not observe as occurring away from the observer or because of limited photo-identification effort. We applied permutation tests (10 000 permutations with 10 trials per permutation) to assess whether social units associated randomly or had preferred/avoided associations (Bejder et al., 1998; Whitehead et al., 2005) by permuting daily association data (social units were considered associated if sighted together during the same day). We investigated the structural function of each social unit in the network through the betweenness centrality, which counts how many shortest paths between each pair of graph nodes pass by a node and measures the extent to which a node lies on paths between other nodes. This metric identifies key intermediary nodes that enhance connectivity and facilitate the transmission of information or behaviours (Vital & Martins, 2009).

To measure social assortment of social units by RPD (that is, the probability of social units to be preferentially associated if sharing a similar RPD; details provided in Appendix 5), we calculated the weighted assortativity coefficient for each of the 2-year subperiods using the function assortment.discrete() from the R package 'assortnet' (Farine, 2014). We compared these observed assortment coefficient values to those calculated from 1000 randomly permuted networks, where the RPD levels were shuffled among the social units to create a distribution of expected assortment

coefficient values under the null hypothesis of no assortment. Finally, we considered the observed assortment coefficient statistically significant if it fell within the 2.5% highest or lowest values of the permutation distribution, using a two-tailed test.

## Diffusion Models

Through an association matrix that provided estimates of the proportion of time social units spent associated, we used the NBDA to quantify the extent to which social network structure explained the spread of the depredation behaviour at the social unit level within the Crozet killer whale population over time (Hasenjager et al., 2021). NBDA models the spread of behaviour as a stochastic process in which, at any given time, each naïve social unit has a learning rate that determines the likelihood of learning the behaviour at that time. The underlying assumption of this analysis was that depredation should spread at a higher rate when socially transmitted between social units spending more time associated than when independently learnt. This effect is represented here by the social transmission parameter (s). Following Hasenjager et al. (2021), we chose the order-of-acquisition model (OADA) as a model better suited for data without precise timing of acquisition that used the order in which social units acquired the behaviour to examine its diffusion through the social network. An acquisition event was defined as the first depredation event during which a given social unit was confirmed present from the photo-identification data. We implemented three OADA models to examine the diffusion of depredation, incorporating a dynamic social network represented as 18 distinct networks, each corresponding to yearly intervals (i.e. 2005–2022) to account for changes in social unit associations over time (Hobaiter et al., 2014). Among these models, two incorporated social transmission (with and without demographic parameters to test whether the migration and/or demography of social units influenced the rate of behaviour acquisition) and one assuming no social transmission (that is, behaviour spread purely through independent learning). We selected the best model using the AICc. To estimate the support for the social transmission best model versus the asocial model, we conducted an AICc comparison using the formula: relative support = $exp(0.5 \times [\text{AICc of asocial model} – \text{AICc of social transmission best model}])$. We also performed a likelihood ratio test (LRT) to evaluate whether adding the social transmission parameter significantly improved the model fit compared with a simpler model without it.

We detected and quantified the influence of horizontal social transmission on depredation diffusion by evaluating summed Akaike weights ($\Sigma\omega_i$), maximum-likelihood estimates of the social transmission parameter (s) and its 95% CIs. We estimated the percentage of events occurring through social transmission (%ST) by fitting the best-constrained OADA model to our data, following the approach outlined by Hasenjager et al. (2021). Specifically, the social transmission parameter (s) and its upper and lower limits allow us to derive the proportion of depredation behaviours attributed to social learning.

## RESULTS

### Acquisition of the Depredation Behaviour by Social Units

The number of social units that had acquired depredation behaviour increased from 7 (41% of all social units) in 2005 to 17 (100% of all social units) in 2014 (Fig. 1a; Supplementary Material Fig. S1; Table S1). The mean number of social units present during a same depredation event significantly increased from 1.80 ± 0.92 units in 2005 to 2.80 ± 1.49 units in 2022 (logarithmic model
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Temporal trends in depredation behaviour among Crozet killer whale social units between 2005 and 2022. (a) Annual cumulative number of social units having acquired depredation behaviour. (b) Annual mean number of social units present during a single depredation event (error bars are the SD of the mean). (c) Annual proportion of fishing days during which killer whale depredation was confirmed or not recorded as absent out of all fishing days.</figcaption></figure>

$R^2 = 0.12, P < 0.001$; Fig. 1b; Supplementary Material Section 1). The overall proportion of fishing days with killer whale depredation varied between 67% and 90% of the fishing days across the years, but no trend was detected over the 2005 to 2022 period (linear model $R^2 = -0.06, P = 0.966$; Fig. 1c; Supplementary Material Section 1).

## Contribution of Social Transmission to Depredation Behaviour

### Behavioural network assortment

The RPD, as estimated from the final model, significantly varied between the social units and the 2-year subperiods (see Supplementary Material Section 2 for model selection and
outputs: Table S2, Fig. S2, Table S3). The social analysis indicated that among killer whale social units (1) SRIs of the overall period (i.e. 2005–2022), were significantly correlated between sightings from the shore of Possession Island and sightings from fishing vessels (Mantel test: R = 0.29, P = 0.004) and (2) preferred/avoided associations occurred during each 2-year subperiods (Supplementary Material Table S4). All the observed social units were connected in a single network, except for the first and the second subperiods (i.e. 2005–2006 and 2007–2008), where social units CR138, CR204 and CR192/CR228 associated with CR195 were connected with no other units in the network (Fig. 2).

Among the first network subperiod, these unconnected units all showed a nonzero RPD value (Fig. 2). Social units CR018, CR016, CR002, CR063 and CR138 were units with high RPD and high betweenness centrality in the early years (before 2010) of the study period (Fig. 2). In the early years, high SRIs (that is, strong associations) occurred between social units with similar levels of RPD (Fig. 2), with a significant correlation before 2014 (Spearman's rho = 0.37, P < 0.001). However, after 2014, this correlation weakened and became less significant (Spearman's rho = 0.19, P = 0.030), despite heterogeneity in RPD remaining among social units (Fig. 2).

<figure><img src="image_5.png" /><figcaption>**Figure 2.** Changes of the social network of the 17 killer whale social units of the Crozet population from 2005 to 2022. Sociograms were produced for 2-year subperiods and included the following: nodes: social units, coloured from a five-level gradient of relative probability to be present during depredation events (RPD, with white [unit not observed], grey [RPD = 0 but unit observed], yellow [RPD < 5%], orange [5% ≤ RPD < RPD mean] and red [RPD ≥ RPD mean]); edges (ties): pairwise associations and thickness indicates the strength of associations calculated as the simple ratio index (SRI); and circles around nodes: the level of betweenness centrality coefficient.</figcaption></figure>
Among social units, assortativity by RPD level was significantly higher than the values from the randomized permutations for the 2-year subperiods before 2014, indicating a homogenisation pattern where social units that associated more had more similar levels of depredation activity. In contrast, these assortativity coefficients were consistently low and were not significantly different from the values from the randomized permutations after 2014, except for the period of 2019–2020 (Fig. 3; Supplementary Material Table S5).

## *Diffusion models*

From the OADA analysis, the best-fitted model was the model assuming social transmission and incorporating demographic processes as covariates ($AICc = 62.94, \sum \omega_i = 0.60$ versus $AICc = 67.01, \sum \omega_i = 0.08$ for null model assuming no social transmission; Table 1). The test statistic for the LRT was 4.13, with $P = 0.042$, indicating strong and significant evidence for the inclusion of social transmission in the model. Additionally, the $AICc$ comparison indicated that the model with social transmission and demographic processes received 2.5× much support as the asocial model.

From this best model, the social transmission parameter estimate was ($s$) = 4.72 with 95% CIs not including zero (95% CI, 0.04–155.41; Table 1; Supplementary Material Fig. S3). Based on this model estimates, 62% (95% CI, 3%–91%) of the social units acquired depredation behaviour through social transmission.

## DISCUSSION

From the long-term monitoring of a population exposed to feeding opportunities from fishing activities in its environment,

this study provides evidence of both social and independent learning contributing to the acquisition of a new behaviour by killer whale social units to exploit these opportunities through depredation on fisheries catches. Horizontal social transmission between units was prevalent during the spreading phase of the behaviour (that is, when not all killer whale social units had yet acquired depredation), influencing both its acquisition and the level of engagement in depredation. Social preferences explained a large part of the behavioural heterogeneity across social units regarding the depredation levels shortly after acquisition. However, the role of social preferences in this heterogeneity decreased past the year all social units had developed the behaviour (after 2014), suggesting other ecological or individual/group factors coming into play.

The results suggest that the social units of the Crozet killer whale population have acquired depredation through the concomitant action of independent and social learning pathways. Photo-identification data were available only from 2003 (that is, seven years after the fishery started in the area) and is restricted to a subset of killer whale depredation events, which potentially leads to missing the exact year when some social units first acquired the behaviour. However, our argument, indicating a combination of these two learning pathways simultaneously, are supported by: (1) the results from the sociograms (Fig. 2), (2) the social assortment (Fig. 3) and OADA models (Table 1) and (3) the increasing number of depredating individuals in the population being typical of a social transmission curve (Amelot et al., 2022), although such type of curve may also result from asocial learning or structural factors within a population (Reader, 2004). Such duality in learning pathways of a new behaviour in response to human activities has been documented in terrestrial mammals, such as elephants engaging in crop raiding (Chiyo et al., 2012) or primates developing tool use

<figure><img src="image_6.png" /><figcaption>**Figure 3.** Assortativity coefficients calculated for 2-year subperiods by the relative probability of social units to be present during depredation events (RPD) in the Crozet killer whale population between 2005 and 2022. The black points represent the assortativity coefficients from real networks and the vertical black lines show the distribution of assortativity coefficients from networks based on permuted data, where social associations are randomized (2.5 and 97.5 percentiles). Blue shaded background: period over which units (depicted as nodes in the social network in the left inside) preferentially associated with other units having similar RPD; orange shaded background: period over which units (depicted as nodes in the social network in the right inside) had no preferences to associate to other units based on their RPD.</figcaption></figure>
<caption>**Table 1**</caption>

Summed Akaike weights (ωi) and maximum-likelihood estimates of social transmission parameters for OADA models with and without social transmission (ST) fitted to the spread of depredation behaviour among the 17 killer whale social units of the Crozet population between 2005 and 2022

<table><thead><tr><th>Analysis type</th><th>Social transmission model</th><th>K</th><th>LL</th><th>AICc</th><th>Σω<sub>i</sub></th><th>Social transmission parameter estimates (s)</th><th>95% CI</th></tr></thead><tbody><tr><td rowspan="3">Order of acquisition (OADA)</td><td>Dynamic network with ST and demographic processes</td><td>1</td><td>30.33</td><td>62.94</td><td>0.60</td><td>4.72</td><td>0.04–155.41</td></tr><tr><td>Dynamic network with ST</td><td>1</td><td>30.96</td><td>64.18</td><td>0.32</td><td>7.69</td><td>0.22–260.17</td></tr><tr><td>Dynamic network with no ST</td><td>0</td><td>33.50</td><td>67.01</td><td>0.08</td><td>(Constrained to 0)</td><td>—</td></tr></tbody></table>

The outputs include the number of parameters (K), likelihood (LL), Akaike information criterion (AICc), summed Akaike weights (Σωi), social transmission parameter (s) and CI for social transmission parameter (95% CI).

(Bandini & Tennie, 2017), and birds like tits learning to open milk bottles (Aplin et al., 2013) or urban parrots learning to open trash bins (Klump et al., 2021). In addition to providing evidence of these pathways for a marine social species, our findings also suggest that horizontal social transmission was initially the prevalent learning pathway, with approximately 40% and 60% of the killer whale social units being estimated as having acquired the depredation behaviour independently and from their preferential associates at the unit level, respectively. While this aligns with previous studies showing the importance of horizontal cultural transmission of behaviours within killer whale populations (Rendell & Whitehead, 2001; Riesch et al., 2012), it also raises questions about the costs and benefits of each learning pathway that have yet to be elucidated for killer whales across social units. Social learning is quicker, less energy-intensive and/or risky but can spread outdated or less accurate information, whereas independent learning, although more reliable, demands more time and effort (Galef & Giraldeau, 2001; Kendal et al., 2005). Independent learning is often driven by innovation, especially in response to environmental disturbances or new stimuli within an ecosystem (Gruber et al., 2019). Environmental factors, like resource distribution and competition, as well as individual/group characteristics (e.g. age, sex, social rank, personality), play a role in determining who innovates and who learns from whom (Reader & Laland, 2001; Smolla et al., 2015). Smolla et al. (2015) suggested that social learning may be more beneficial when resources are uneven yet predictable, while independent learning is favoured in unpredictable or evenly distributed environments. Evidence of social units independently acquiring the depredation behaviour in our study suggests that in certain units, the individual initially learning depredation did so without direct social interactions with individuals from other social units. Within these units, the behaviour likely indeed originated from an innovator before spreading among members via observation or imitation, as shown for the learning of new behaviours in both captive and free-ranging killer whales (Abramson et al., 2013; Esteban et al., 2022). As such, even when killer whale social units appear to acquire the behaviour independently, social learning remains a key mechanism within these units. This interplay between the potential for innovation of individuals and the diffusion of new behaviours within social units may contribute to the diversity of foraging prey and behaviour observed worldwide in this species (Ford, 2019).

The contribution of horizontal social transmission to explain the engagement level in depredation behaviour by killer whale social units was high when this behaviour was still spreading within the population, but low or absent once all units had acquired it (from 2014; Fig. 3). This finding is supported by a positive correlation between the strength of associations between social units and similarity in their probability to be involved in depredation (strong assortativity), found before 2014 (that is, the year all social units had acquired depredation; Amelot et al., 2022; Fig. 3). At Crozet, killer whales were reported depredating on fishery catches as soon

as the first fishing vessels began operating in the area in 1996 (Tixier et al., 2020). Which social units initially acquired the depredation behaviour and were at the origin of its spread across the population through horizontal social transmission is unknown because of a lack of photo-identification information in the early years of the fishery. However, as indicated by the results, some social units were found to have a high betweenness centrality in the social network of the population (Fig. 2), acting as key structural nodes or so-called 'cultural brokers' linking otherwise unconnected social units. Based on what has been shown in other social species (Donaldson et al., 2012; Krause et al., 2007), this suggests that social units at the centre of networks may have had a key role in the social spread of the depredation behaviour through the funnelling of information flow between different social units or controlling the access or the quality of information (Vital & Martins, 2009). As heterogeneity in the level of engagement in depredation behaviour (e.g. variation in the frequency and the spatial range at/in which they use the behaviour) was reported past 2014 (Aguuin et al., 2024; this study: Fig. 3), this result suggests that once the behaviour is widespread within a population, factors other than social transmission determine this heterogeneity. These factors may include variation in the natural spatial distribution of social units (Baird et al., 2019; Tixier, Lea, et al., 2021), in their prey preferences (Samarra et al., 2017; Tixier et al., 2019) and/or intrinsic characteristics (e.g. experience, personality, etc.) of decision-making individuals within social units, as shown in killer whales and other social species (McComb et al., 2011; Toscano et al., 2016).

The acquisition of a new behaviour in response to fishing activities through social transmission has implications for the conservation of social odontocete populations involved and their ecosystem, as well as for the management of conflicts with fisheries emanating from depredation. First, the acquisition and the spread of depredation within populations, as a behaviour allowing individuals to feed on highly energetic prey at low foraging cost, may lead to the loss of behavioural traditions. For species with established cultural transmission, Gruber et al. (2019) predict that the higher the impact of humans on their habitat, the more likely individuals are to lose existing behavioural traditions because of new ones emerging in response to novel anthropogenic stimuli. Among social odontocete populations for which foraging behaviours associated with anthropogenic food sources are likely socially transmitted (Donaldson et al., 2012; Esteban et al., 2016; Schakner et al., 2014), this could lead to a decline in traditional foraging techniques, for instance in bottlenose dolphins, *Tursiops* sp., using tools to catch fishes (Mann et al., 2008) or intentional stranding in killer whales to catch prey on the shore (Guinet, 1992). While human-induced changes can foster the development of new cultural traits such as depredation, there is a risk of loss of behavioural diversity as populations face similar threats (Gruber et al., 2019; Whitehead et al., 2023). Second, the spread of depredation across social units within populations may increase the number of individuals exposed to the risks associated with the behaviour.
Among these risks, individuals may be exposed to lethal retaliation practices (e.g. shooting or use of explosives) used by fishers to repel the whales from their fishing gear (Dahlheim et al., 2022; Tixier et al., 2010). Third, for these social units structuring killer whale populations, social transmission can also change interactions with habitat use by inducing a dietary shift, increasing reliance on depredated fish, and this may lead to stronger effects of food pro- visioning on their demographic rates (Tixier et al., 2017) and on their ecological role in the ecosystem (e.g. alteration of their predatory pressures on other prey; Clavareau et al., 2023). Lastly, the spread of depredation across social units may lead to an increased probability for fishing vessels to be subject to odontocete depredation over fishing areas (Baird et al., 2019; Schakner et al., 2014) and, consequently, to a decreased effectiveness of avoid- ance strategies often recommended by managing authorities. For instance, it could result in increased aggregations of social units during the same depredation events (as observed in this study), potentially reshaping the population's social dynamics as observed in bottlenose dolphins, *Tursiops aduncus* (Chilvers & Corkeron, 2001), though the effects of fisheries on social structure can vary across species and/or contexts (see Ansmann et al., 2012).

In conclusion, empirical research on how new behaviours emerge and are engaged within top predator populations in response to human activities remains scarce. This study addressed this gap by showing the concomitant role of two learning pathways (that is, social versus independent) in the acquisition of the depredation behaviour within a killer whale population, though the social learning was prevalent. While the social transmission after the acquisition may explain part of the heterogeneity observed in this behaviour across social units, other factors influ- encing the propensity of social units to engage in depredation have still to be elucidated. Yet, the findings suggest that fisheries have varying levels of impact on the life-history traits of individuals within the population, and this highlights how human activities, such as fisheries, act as catalysts for behavioural and cultural shifts within animal populations. As human–wildlife interactions continue to intensify globally, understanding how species respond to these pressures, especially those with complex social structures, is increasingly vital for conservation.

## Author Contributions

**Christophe Guinet:** Writing – review & editing, Visualization, Validation, Supervision, Investigation, Data curation, Conceptualization. **Erwan Auguin:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Formal analysis, Conceptualization. **Johann Mourier:** Writing – review & editing, Visualization, Validation, Methodology, Formal analysis. **Eric E.G. Clua:** Writing – review & editing, Visualization, Validation, Project administration, Funding acquisition. **Nicolas Gasco:** Writing – review & editing, Resources, Data curation. **Paul Tixier:** Writing – review & editing, Visualization, Validation, Supervision, Methodology, Investigation, Data curation, Conceptualization.

## Data Availability

The data used for the study are available at: https://doi.org/10.6084/m9.fishshare.28595852.

## Declaration of Interest

The authors declare no competing interests.

## Acknowledgments

This work was funded by the ANR project 21-CE03-004 ETHO-PREDATOR, coordinated by Eric E.G. Clua. We are grateful to the Muséum National d'Histoire Naturelle or Paris and especially Guy Duhamel, Clara Péron, Patrice Pruvost, Alexis Martin and Charlotte Chazeau, for consolidating and providing data from the PECHEKER database. Special thanks to all the photographers based at the Alfred Faure research station in Possession Island (Crozet Archipelago) and fishery observers who collected photo-identification images. Additional support was provided by the Terres Australes et Antarctiques Françaises (TAAF), the French Polar Institute (IPEV Program 109, coordinator: Christophe Barbraud at CEBC-CNRS) with the help of Karine Delord and Dominique Besson (CEBC-CNRS), the Reunion Island Fishing (SARPC & Fondation d'Entreprise des Mers Australes) and the Direction des Pêches Maritimes et de l'Aquaculture (DPMA). We wish to thank the editor and the two anonymous referees whose comments greatly improved the manuscript.

## Supplementary Material

Supplementary material associated with this article is available at https://doi.org/10.1016/j.anbehav.2025.123228.

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## Appendix 1. Depredation cues

The depredation cues visible from the surface included the presence of killer whales foraging within a 500 m radius of the vessel, often surrounded by seabirds and fish oil slicks at the surface when surfacing, as well as the retrieval of fish heads or lips on the hooks of the longline while killer whales were around (Tixier et al., 2010).

## Appendix 2. Details of photographs from fishing vessels

Photographs of killer whales depredating fish on longlines were available for a subset of all killer whale depredation events, that is, 30% of all killer whale depredation events recorded from licensed fishing vessels by fishery observers, with the possibility that killer whale depredation events also occurred around fishing vessels operating illegally in the area.

## Appendix 3. Validation of models' assumptions and residual distributions

We checked and validated all models' assumptions following the DHARMA protocol, simulating 1000 datasets from the fitted model to test if the distribution of the scaled residuals deviated from the expected distribution (Hartig, 2022). We accounted for nonlinear trends by applying log transformations when necessary.

## Appendix 4. Details on the data for relative probability of depredation (RPD) estimation

To prevent overestimation, we excluded sightings of the same social unit within 12 h of their first sighting around the same vessel, since killer whales tend to follow fishing vessels and depredate on consecutive longlines (Cieslak et al., 2021). This adjustment restricted the data used for the model to 1198 sightings (82% of all sightings). From this data set, we used a generalized linear model (GLM), with a binomial distribution and a logit link function (Bolker et al., 2009; Zuur et al., 2009). The response variable was the occurrence of social units during sightings. For each sighting, multiple records were created, one for each social unit, with a binary response variable indicating presence (1) or absence (0) of the unit during the sighting. We predicted the relative probability of each social unit to be present during depredation events from the best model outputs, and we used the pairwise comparisons of estimated marginal means (EMMs, method 'Holm'–package 'emmeans' in R (Lenth, 2023)) as a post hoc test of differences in the probability between units.

## Appendix 5. Details on the social assortment analysis

The assortativity coefficient evaluates the correlation between the RPD (relative probability of depredation) and the likelihood of social units to associate, which allows us to test for homophily (that is, assortment: the tendency for nodes that share a trait to be connected) in depredation behaviour. The assortativity coefficient has a general form (Farine, 2014):

$$r = \frac{\sum_{ij} (A_{ij} - k_i k_j / 2m) x_i x_j}{\sum_{ij} (k_i \delta_{ij} - k_i k_j / 2m) x_i x_j}$$

where kiðij is the degree of 'i' excluding its connection with 'j'. Aij is the actual number of edges between i and j. Notice that ki kj/2m is
the expected number of edges between vertices i and j in the configuration model assumption. This is a form of the Pearson correlation coefficient. The coefficient is large (approaches 1) if nodes with similar values are more connected and small (approaches -1) when similar nodes are less connected. The value is 0 when edges are random with respect to node values. Assortativity is assessed by comparing the observed number of connections between similar nodes to the expected number under a null model preserving node degree distribution. The weighted assortativity coefficient can be achieved by replacing the count of edges in Newman's discrete coefficient (Newman, 2002) with their weights as a proportion of the total weight of the graph (more details in Farine, 2014).

The weighted assortativity coefficient offers a more robust and comprehensive framework for analysing assortment (Farine, 2014). This approach minimises the impact of sampling errors, random interactions and weak associations when estimating social assortment. The assortativity coefficient ranges from -1 to 1, where positive values indicate a tendency for social units with similar RPD values to associate (homophily), negative values suggest social units with dissimilar RPD values are more likely to associate (heterophily) and values around zero imply no particular pattern of association based on RPD. By comparing the observed assortativity to a distribution of values generated through random permutations (where RPD values are shuffled), we can determine whether the observed pattern is statistically significant or could be attributed to random chance.