Leading-following behavior is prominent in different species to transfer information from informed to naïve individuals1,2,3,4,5. Those individuals who actively explore their environment, gather private information about the availability or the location of a certain resource, and subsequently lead naïve individuals to these resources6. By following a leader, naïve individuals gather information socially and become informed without having to spend prior search effort7. When grouping at the resource is beneficial, e.g. during communal roosting, informed individuals benefit from leading naïve individuals as this increases the likelihood of conspecifics being present at the resource8.

A natural question in the study of leading-following behaviour is how individuals assume their roles as leaders, followers or both. Studies in collective motion have already reported that distinct leadership roles can emerge if some individuals are more active or better informed than others4,9 or stand to gain more from imposing their preferences10,11. The presence of a small fraction of informed leaders has also been shown to be sufficient in guiding the movement of large groups with great accuracy in both human and animal societies12,13. Some animal studies have even suggested that in addition to immediate cost and benefits, leadership is a personality trait independent of differences in information or knowledge of the environment (see Johnstone and Manica14 and references therein). One distinct characteristic of field studies on collective motion, however, is that group movements are tracked continuously5,15. Consequently, factors such as proximity, association and communication patterns or dominance, are observed with high enough resolution to inform a reliable picture of how leading-following behavior emerges and what roles different individuals assume.

Continuous tracking, however, is resource intensive and for some social systems prohibitively laborious, such as in the case of fission-fusion systems, in which the group is only temporarily cohesive. Therefore many field experiments collect only discrete observational data in the form of individual recordings of presence at experimental locations. To study leading-following behaviour in such cases, one needs to (a) first reconstruct that behaviour from the available data and (b) only then quantify the resulting leading-following patterns. It is the aim of this paper to propose a sound methodology to accomplish both of these goals.

To demonstrate our methodology, we focus on two colonies of Bechstein’s bats (Myotis bechsteinii), a forest-living, European bat species. Using discrete observational data spanning five years, we infer leading-following events by parametrizing empirical insights from field studies and calibrating these parameters from the existing data. In this way, we achieve a statistically robust inference that is entirely data-driven.

After inferring such leading-following events, we then turn to the established field of social network analysis16. We represent the information transfer resulting from leading-following conceptually as an information network and then quantify individual impact in it. In the resulting networks individuals are represented as nodes and their leading-following events as directed and weighted links, where the weights indicate the frequency of such events. This abstraction allows us to further analyze topological characteristics of such networks. On the level of the animal group (here, bat colony), this includes features such as connectedness, i.e. to what extent the information flow resulting from leading-following behavior permeates the whole system. On the individual level, it allows us to calculate centralities to proxy the importance of the nodes, which translates to the influence of specific bats on the information flow.

We note that social network theory has already transcended the human domain and has become widely accepted as an important conceptual framework for studying social interactions in animal groups in general17,18,19,20. In Bechstein’s bats, social network theory has unveiled the presence of long-term social relationships despite the high fission-fusion dynamics of the colonies, thereby imparting novel insights on the relation between cognitive abilities and social complexity21.

Therefore, we see our two-step approach of quantifying individual influence in leading-following as a sound method to study leading-following behavior based on discrete observations. As we also point out in the concluding discussions, we see the potential for a much broader application to studying leading-following behavior in different species, as well.

Study animals: Bechstein’s bats

Coordination in roosting behavior

During summer adult female Bechstein’s bats form colonies to communally raise their young (adult males are solitary; Kerth and König22). Such maternity colonies comprise 10–50 individuals, have a very stable individual composition, and are highly heterogeneous with respect to the age, reproductive status and the degree of relatedness among colony members21,23,24. Colonies switch communal roosts (tree cavities and bat boxes) almost daily and regularly split into several subgroups that use separate day roost21,22. Communal roosting provides the females and their offsprings with grouping benefits, such as energetic advantages through clustering (e.g. social thermoregulation25,26).

At the same time the frequent roost switching forces the female Bechstein’s bats to regularly explore new potential roosts during their nightly foraging trips and to coordinate their movements among day roosts in order to avoid permanent fission of the colony27,28. Experienced individuals, who have discovered the locations of suitable roosts through independent exploration, transfer their private knowledge to naïve conspecifics by leading them to these locations3. Such leading-following events take place when one or several experienced bats arrive together with one or several naïve bats at a box at night. Information transfer about suitable roosts provides benefits to both the leading and the following bat. By leading conspecifics to potential roosts, an experienced individual increases the likelihood of communally roosting with conspecifics. At the same time, by following experienced individuals, naïve bats gather information socially without the need to spend prior search effort.

Field data collection

From 2007 to 2011, we studied two colonies (BS and GB2) of Bechstein’s bats within their natural home ranges located in two forests near Würzburg, Germany (Figure S1, left). The BS colony was not monitored in 2008. Since 1996, all adult female bats in both colonies have been individually marked with individual RFID-tags in their first year of life29. Each RFID-tag is programmed with a unique 10-digit ID that can be identified and recorded by automatic reading devices3. The study period in each year was between the beginning of May and end of September. In that time, the colonies’ home ranges were equipped with about 20-30 experimental bat boxes per year in addition to a large number of already existing boxes (about 100; Fleischmann et al.27; Figure S1, right). These boxes were to serve as day roosts, similar to natural roosts in tree cavities, in which the Bechstein’s bats spend the day. All experimental boxes were equipped with RFID-loggers that recorded the bats’ nightly visits3,27. In this way, every time a bat passes the entrance of an experimental box, its unique ID would be read and stored by the reading device without disturbance to the individual.

At the beginning of the study period in each year, the experimental boxes were placed within the home ranges and thus their locations were unknown to the bats until the first colony members discover them through private information gathering. Moreover, each box was positioned within 300 meters of day roosts used in the previous year.

Importantly, not all experimental boxes were discovered by the colony in a given year. Moreover, not all discovered and visited experimental boxes were subsequently used as day roosts.

Our datasets, thus, consist of the yearly recordings of the reading devices from all experimental boxes for each of the two colonies in each of the 5 years. Each recording contains a timestamp and the unique 10-digit ID of the bat who activated the reading device. An example dataset is shown in Table S1 in the supplemental material. Table 1 shows a summary of the total number of readings and the number of installed, discovered and occupied experimental roosts, for each colony throughout the years.

Table 1 Data summary.


Inferring leading-following networks

Defining leading-following events

Unlike studies on collective motion where group movement is tracked continuously5,15, our datasets contain only discrete records of bat appearances at experimental boxes. Quantifying individual influence is, thus, contingent on a rigorous method for inferring leading-following events from discrete recordings of animal occurrences. To denote the information that individuals possess about the location of experimental boxes, we refine the nomenclature used by Kerth and Reckardt3. An individual bat is said to be naïve at time \({{{\mathbf {t}}}}_{{{\mathbf {1}}}}\) regarding a given box, if it has not been recorded by the reading device in that box for all times \({{\mathbf {t}}}<{{{\mathbf {t}}}}_{{{\mathbf {1}}}}\). Similarly, an individual bat is considered experienced at time \({{{\mathbf {t}}}}_{{{\mathbf {2}}}}\) regarding a given box, if it has been recorded in that box at any previous time \({{\mathbf {t}}}<{{{\mathbf {t}}}}_{{{\mathbf {2}}}}\). We define a leading-following (L/F) event to a given box at time \({{{\mathbf {t}}}}_{{{\mathbf {3}}}}\) as the joint visit of two individuals—one naïve and one experienced at time \({{{\mathbf {t}}}}_{{{\mathbf {3}}}}\). The details of how joint arrivals are calculated are presented later in the paper.

In case more than two bats arrive jointly, we form all possible L/F pairs consisting of one naïve follower and one experienced leader. In case the leader and the follower were recorded multiple times, we take those times that minimize the difference between their appearances in the dataset (see Table S2 and associated explanation). Finally, we refer to time_difference of an L/F event as the absolute difference between the recording times of the leader and the follower.

With this definition of L/F events, the actual inference of L/F event patterns from the data relies on three parameters: (1) lf_delay: the maximum allowed time difference (in minutes) between consecutive recordings of a leader and a follower, (2) turnaround_time: the minimum time (in minutes) an experienced bat in an L/F event needs to potentially become a leader, i.e. the time needed to find and lead followers, and (3) occupation_deadline: the hour in the morning on the day of a box occupation, after which subsequent recordings from this box are ignored because of swarming behavior (example of local enhancement, Kerth et al.3, Kerth and Reckardt28).

Calibrating parameter values

It is important to note that each of the three parameters affects the inference of L/F events differently. Values of lf_delay that are too large would lead us to incorrectly define many visits of an experienced and naïve bats as joint visits, i.e. as legitimate L/F events, even those that occur in different days. Too small values of turnaround_time, on the other hand, will force us to “break” one L/F event with one leader and multiple followers into separate L/F events where the previous followers are now falsely deemed as leaders. Similarly, if occupation_deadline is too late in the morning, a lot of the joint visits due to swarming will be incorrectly inferred as L/F events. We discuss the parameter influence on the inference procedure in more detail in Section S.3.

To choose appropriate values for the three parameters we resort to a purely data-driven process based on comparing the distributions of L/F event time differences statistically. This represents a more rigorous approach compared to an otherwise subjective calibration based on observations often used in analysis of field studies.

Empirical research in the field of information transfer in Bechstein’s bats has suggested 3 min for lf_delay and 3 a.m. for occupation_deadline as a reasonable rule of thumb (Kerth and Reckardt 2003). We build upon these heuristics by (a) introducing an additional parameter turnaround_time defined above and (b) by comparing the distributions of time differences of all L/F events (Fig. 1).

Figure 1
figure 1

L/F time differences for the GB2 colony in 2008. Histograms show the absolute differences between the times at which the leader and the follower were recorded in all identified L/F events. Parameters: turnaround_time = lf_delay = 3 min (both plots),occupation_deadline = 2 a.m. (left) and occupation_deadline = 3 a.m. (right). Insets indicates the total number of identified L/F events.

Note that any combination of the three parameters is a 3-tuple, which generates a set of L/F time differences from all identified L/F events in the dataset. In Fig. 1 we show two-dimensional histograms of L/F time differences for fixed values of lf_delay = turnaround_time = 3 min, and occupation_deadline = 2 a.m. (left) and occupation_deadline = 3 a.m. (right). As there is no objective method to quantify the behaviour underlying each of the parameters, we argue that L/F time differences best capture the effect that varying the parameters has on the L/F events we identify. For example, a visual inspection of Fig. 1 hints that increasing occupation_deadline from 2 a.m. to 3 a.m. does not change the time difference distributions. This implies that swarming has not yet set in (otherwise, we would expect quantitatively more events with longer time difference), and the additional L/F events on the right-hand side are genuine. Consequently, we would prefer occupation_deadline = 3 a.m., as it increases our sample size. Section S.4 details the expected effects of swarming on the distributions of L/F time differences.

The core of our method revolves around pairwise testing for statistical difference in the distributions of L/F time differences generated by different values of the 3-parameter tuple. We start from the reasonable default values mentioned above and summarize the result of the calibration in Tables 2 and 3.

Table 2 GB 2 colony in 2008 with lf_delay = 5 min.

To generate sufficient sample sizes for the comparison, the dataset we chose to analyze was the GB2 colony in 2008 (Table 1). The reason is that, in 2008, the colony had the highest number of discovered and occupied boxes, the second largest colony size, and a large amount of individual readings. Therefore, we expected to identify the largest number of L/F events from this dataset, and thus obtain the most robust parameter values.

In Table 2, lf_delay is fixed at 5 min, while occupation_deadline is varied in {2 a.m., 3 a.m., 5 a.m., 8 a.m.}, and turnaround_time – in {2, 3, 5, 7, 9} min. For each value of turnaround_time (rows in the table), we compare the time difference distributions (\({\mathcal {X}}_{i}\)/\({\mathcal {Y}}_{i}\)) between all possible pairs of occupation_deadline. The comparison is done via a bootstrapped Wilcoxon rank-sum test on the null hypothesis that the two distributions are the same, against the two-sided alternative \({\mathcal {H}}_{1}\), and the one-sided alternative \({\mathcal {H}}_{2}\) that \({\mathcal {X}}_{i} < {\mathcal {Y}}_{i}\). Each table cell shows the p value for the two-sided and one-sided test, respectively.

As an example, fixing \(\texttt {turnaround\_time}=2\) min, we see that the distribution of L/F time differences for occupation_deadline at 2 a.m. is not statistically different from the distribution with occupation_deadline at 3 a.m. (p value = 0.725). This is an indication that the nature of the identified L/F events is invariant to the later deadline, hence it is unlikely that we have inadvertently included swarming effects. Further inspection of the table reveals that qualitative changes in L/F time differences occur when occupation_deadline = 8 a.m., but not for the other pair-wise comparisons. The one-sided test indicates the type of these changes, namely that L/F events inferred up to 8 a.m. on the day of occupation, tend to have larger time differences compared to earlier occupation deadlines. This is in line with the reasoning in Section S.4 of the Supplementary Material and implies the presence of swarming effects. Therefore, occupation_deadline = 8 a.m. is likely too late.

Moreover, this conclusion holds when varying turnaround_time, as well. The impact of this parameter on the L/F time differences seems to be small, in the range considered. The effect of turnaround_time is primarily on the number of identified L/F events, as assuming larger recruitment delays excludes events where the leader found a follower relatively quickly (Table 3).

Table 3 Number of identified L/F events for the GB2 colony in 2008 with different values of the three parameters.

Considering these arguments, we see that lf_delay = 5 min, turnaround_time = 3 min. and occupation_deadline = 5 a.m. provide the best trade-off between maximising the number of identified L/F events while still keeping the distribution of L/F time difference undistorted by swarming. We also see these values as improvements over the common heuristics mentioned in the beginning of the section.

Constructing leading-following networks

With the parameters calibrated following the above procedure, we identified all L/F events in each of our datasets (that is 5 datasets for colony GB2 and 4 datasets for colony BS2 for all years, Table 1).

We then constructed directed and weighted leading-following (L/F) networks from each dataset. In these networks, a node represents an individual bat and a link between two nodes indicates their involvement in a leading-following event. More specifically, links are directed. A directed link from node A to node B, denoted as A \(\rightarrow\) B, means that individual A followed individual B to a given experimental box. The weight of this directed link is the number of times that A followed B (to different experimental boxes) during the study period in the respective year. Note that in constructing these L/F networks, we ignore the target box of each L/F event and simply sum up the number of L/F events to compute the link weights.

We also compute the number of weakly connected (WCC) and strongly connected components (SCC). A WCC of a network is a sub-network in which any node can be reached from any other node, either by a link between these two nodes, or by following a sequence of links through other nodes, regardless of the direction of these links. Similarly, a SCC is a WCC with the additional restriction that the direction of the links must be respected when connecting any two nodes. As we explain in the next section, these two measures are particularly important for judging the extent to which information can spread in a network.

Social network analysis

Quantifying individual influence

We can now use the topology of the constructed networks, i.e. the relation between nodes expressed by their links, to characterize the position of individuals in such a network. Our aim is to identify those nodes, i.e. individual bats, that are most influential in leading other bats. In social network analysis, the importance, or influence, of a node in a certain dynamical process flowing through the network is referred to as centrality. There are various centrality measures in use, and each makes certain implicit assumptions about the dynamical process flowing through the network30. Choosing a centrality measure is, thus, context-dependent (see Fig. 2). An improperly selected centrality metric, can lead to losing the ability to interpret the measure correctly, this way deducing wrong answers.

Figure 2
figure 2

Differences between the three candidate centrality measures. The centralities for each measure are indicated next to each node. (a) In-degree centrality. Here, only direct influence is measured. Individual 4 is most influential, as she spread information to three different individuals. Individuals 1, and 5 with one follower each, have still equal importance. (b) Eigenvector centrality. Since individuals 2 and 3 have no followers, they are attributed zero influence, and thus contribute nothing to the influence of their leader, individual 4. In turn, 1, 4, and 5, each have one follower of non-zero importance, hence they have the same eigenvector scores. (c) Second-degree centrality with \(\alpha=0.5.\) Individual 4 has a higher centrality than her in-degree score, as we account for the indirect contribution of individual 1 \((3 + 0.5 \times 1 = 3.5).\) However, 5 is now more important than 1, because 4 contributes to 5 indirectly \((1+0.5 \times 3 = 2.5).\)

In-degree, eigenvector and second-degree centrality

In our case, an appropriate centrality measure must reflect the notion of individual influence in spreading information about suitable roosts. If influence is best proxied by the total amount of roosts that a given bat made known to the colony, then a suitable centrality measure is the in-degree centrality (Fig. 2a). This quantity measures individual importance as the total number of bats that an experienced bat spreads information to directly, i.e. the number of L/F events in which an individual participated as a leader. In-degree centrality is, thus, calculated as the weighted sum of all directed links that point to a given experienced individual.

In-degree centrality measures the total number of leadings, i.e. direct influence, without considering how the information distributed by a leader to its followers propagates further through the colony. To also account for such indirect effects, an alternative centrality measure is eigenvector centrality (Fig. 2b). In a social network, a node has high eigenvector centrality if it is pointed to by nodes that themselves have high eigenvector centralities. In other words, an experienced bat leading a few bats, who themselves lead a lot can be more influential than a bat leading many other bats who in turn never lead. The computation of eigenvector centralities is presented in Section S.5 of the Supplementary Material.

The in-degree and eigenvector centralities represent two extremes, the former measuring exclusively direct influence, and the latter additionally measuring all possible indirect ways, in which information can flow from one individual to all the rest. Eigenvector centrality, however, considers information chains of all lengths to be of equal importance, regardless of the target experimental box. An information chain of length k is simply a sequence of k L/F events identified for a given network, in which the follower in a previous L/F event is the leader of a later L/F event.

For example, two identified L/F events A \(\rightarrow\) B and C \(\rightarrow\) A, constitute a chain of length two (in addition to forming two separate chains of length one). Assuming both L/F events were to the same experimental box, then B ought to obtain direct importance from having led A, but also indirect contribution, for were it not to B, A would not have learned about this box and thus could not lead C to it. This assumption is not entirely correct, however, since it is possible, though unknowable, that A would have found the roost by its own exploration, or that A “forgot” the information obtained from B, and re-visited the box before leading C. The latter issue is exacerbated with the length of the event chains we consider. However, if the two L/F events were not to the same target box, B should not obtain any indirect benefits from the second L/F event. Note that in-degree centrality only considers L/F chains of lenth 1, i.e. direct influence.

Since we construct aggregated L/F networks (i.e. links represent leading-following, disregarding the target box), we risk attributing too much importance to individuals when using eigenvector centrality. The metric will simply grow with the length of the chain and individuals who are part of longer chains will tend to be quantified as more influential. This would risk distorting individuals’ influence scores, since it is highly unlikely that a long L/F chain had the same target box for all L/F events in the chain.

We again use our data to inform the proper balance between direct and indirect influence, and thus to choose the right centrality metric. Figure 3 shows the relative frequency, aggregated over all datasets, of observing chains of L/F events. This frequency can be interpreted as the probability of finding chains of a given length. As the inset in Fig. 3 demonstrates, the probability distribution resembles an exponential distribution. The plot further indicates that chains longer than 16 did not occur in any of the datasets we have. More importantly, event chains of length up to two constitute more than 80% of all lengths observed, and the probability of longer chains decreases drastically. We, thus, posit that limiting the influence computation to L/F chains of maximum length of two reflects properly the majority pattern observed in the data. Hence, it represents a proper heuristic that minimizes the risk of inflating indirect influence scores by including long L/F chains that do not represent genuine information spread about the same roost.

Figure 3
figure 3

Probability distribution of the lengths of L/F event chains, calculated over all nine datasets. Inset: log-linear plot of the data.

For this reason, we define a new metric—second-degree centrality (Fig. 2c)—which computes centrality as the in-degree of the focal individual and the sum of the in-degrees of its followers, weighted by a factor \(\alpha\) (in that sense the followers of one’s followers are its second-degree followers). This reflects our observation that chains of length up to two constitute the majority in all datasets. We, thus, use second-degree centrality with \(\alpha =0.5\) as the main measure for quantifying individual influence. We will, however, keep in-degree and eigenvector centrality for comparison purposes to make sure that our heuristic metric does indeed reflect a balance between the two extrema and produces consistent results.

All analysis was done in the R programming language.

Ethics approval

Handling and tagging of the bats were conducted under the permits for species protection (55.1-8642.01-2/00) and animal welfare (54-2531.01-56/06; 55.2-2531.01-79/10; 55.2-2531.01-47/11) that had been issued by the government of Lower Franconia.


Constructing the L/F networks

As already explained, an L/F network illustrates all detected L/F events for a given colony in a given year, where nodes represent individual bats and directed links represent leading-following events. The data is aggregated over the whole season, thus the width of the links indicates the number of events in the dataset.

We construct the networks for all datasets and present a summary of their salient network characteristics in Table 4, regarding their degree of connectedness. Network density is defined as the fraction of inferred L/F events out of the maximum possible number of L/F events for that network. For example, the L/F network for the GB2 colony in 2007 consists of 31 individuals, hence the maximum possible number of L/F events is \(31\times 30=930\), which yields a network density of 0.06.

Table 4 Topological characteristics of the leading-following networks from the GB2 and BS colonies.

We see that the two colonies differ in this respect through the years. While the L/F networks for the BS colony display high density and connectivity for all study years, the L/F networks for GB2 colony in the years 2007, 2009 and 2010 have low density consistent with the fewer L/F events observed. Therefore, to calculate the importance of each individual, we use only the cyan-coloured datasets in Table 4, as they provide the most reliable sample sizes of detected L/F events for statistical analysis.

If we focus only on these datasets, we also find that the information spreading through the colony was prominent enough to give us enough data for quantitative conclusions. We see that their respective L/F networks are weakly connected, that is there is only one weakly connected component, which means that all individuals participated in L/F events, i.e. in information spreading. Moreover, these networks consist of only a few (1-3) strongly connected components (SCC). Within an SCC, each individual can be reached from any other individual by following (a chain of) directed links. In most of the chosen cases, the size of the largest SCC is similar to the total number of nodes, which means that the vast majority of individuals participated as both leaders and followers. Otherwise, one could reach a given individual through a directed chain, but will not be able to connect from this individual back to the network via a directed chain. Hence, individuals would be part of a weakly connected component (WCC) because they are either followers or leaders, but they would not be part of a SCC.

Quantifying individual influence

As an illustration, Fig. 4 shows one L/F network for the GB2 colony in the year 2008. It is clear that individuals differ remarkably with respect to their importance, as reflected both by their in-degree centrality (size of the nodes) and their eigenvector centrality (node color). It is also evident that there are correlation between in-degree and eigenvector centrality, as visible for the four individuals in the center.

Figure 4
figure 4

Aggregated leading-following network for the GB2 colony in 2008. Nodes represent individual bats (indicated by a hexadecimal number inside the circle). Directed links represent following behaviour. Node colors indicate eigenvector centrality, whereas node sizes indicate in-degree centrality. The four individuals with highest eigenvector centrality are shown in the middle. Note that for the sake of illustration, links show only unique L/F events. I.e. leading-following between the same leader and follower, but to different roosts, are omitted to maintain the readability of the graph. Total number of unique L/F events is 262, while the total number of L/F events, including multiple leading-following between the same individuals, is 321 (Table 4).

We now use the constructed networks to quantify the importance of individuals for spreading information about suitable roosts. To this end, we compute the three different centrality measures introduced in “Social Network Analysis” section—in-degree centrality, eigenvector centrality and our reference measure, second-degree centrality.

Figure 5 shows the results of each of these measures separately for the colony GB2 for the year 2008. If we compare the absolute values of the centralities, we find that influence scores are heterogeneous with a majority of individuals exerting low to mid influence and a minority having high influence. Importantly, this result holds for all considered datasets. We can use the absolute values to determine the relative importance, by ranking individuals according to their second-degree centrality. The results are shown in Fig. 6, where the diagonal indicates increasing rank, i.e. decreasing importance. In order to determine whether these results are robust if instead of second-degree centrality the other two measures are used for the ranking, we have provided the respective ranks in the same plot. As Fig. 6 shows, the three proposed centrality measures produce a highly consistent ranking of individual influence.

Figure 5
figure 5

Ranked individual influence of bats of colony GB2 in year 2008. The x-axis displays the last four digits of a bat’s unique identification number. The y-axis displays the rank according to the second degree centrality (square symbols) in increasing rank order (rank 1—highest centrality). For each bat we additionally plot its rank when importance is quantified as in-degree (circle symbols) and eigenvector centrality (cross symbols). Overlap of the three symbols indicates that the given individual has the same rank, regardless of the centrality measure used. For the individual centrality values see Figure S2.

Figure 6
figure 6

Individual influence quantified according to the three centrality measures introduced in “Social Network Analysis” section: (top left) in-degree centrality, (top right) eigenvector centrality, (bottom) second-degree centrality (top left). For the calculation, the L/F network constructed from the dataset of colony GB2 in year 2008 was used. The x-axis displays the last four digits of a bat’s unique identification number.

To verify this finding, we have extended the above analysis to all datasets indicated in Table 4. For each dataset, we have then calculated the Pearson correlation between the rankings obtained from the three centrality measures. The results are given in Table S.7 in the Supplementary Material. We find that for all datasets the Pearson correlation is very high for all combinations. That means that when considering aggregated measures, such as rankings, second-degree centrality produces consistent rankings. However, as we have argued, it differs from in-degree and eigenvector centrality on the individual level, as it more accurately captures the extent to which the information about a given roost travels along L/F event chains observed in the data.


In this paper we have provided a systematic data-driven method to quantify individual contributions for active information transfer in the form of leading-following behavior. We demonstrated our approach on datasets of discrete recordings from two colonies Bechstein’s bats.

Individual contributions in information transfer can tell us a lot about the complexity of social organizations in different animal systems. For example, in African elephants, a single matriach leads a group and the group members profit from following her as she has long-term experience31. In primates, the individual influence of group members can depend on the context, and may range from a single dominant individual who influences where a group moves to, to a more widely distributed influence on travel destinations among group member32,33.

We have addressed two main challenges present when working with datasets of discrete observations. First, we presented a robust method to infer genuine leading-following events from raw recordings of individual presence and second we applied social network theory as a conceptual framework to represent the information flow from leading-following and to ultimately quantify individual influence.

Regarding the first challenge, we note that most field experiments, including ours, are limited by the state-of-the-art passive RFID-tagging, which only records presence data. There is a more advanced technique15 that uses a proximity sensor system to continuously track the leading-following behaviour between female bats and their juvenile to suitable roosts. However, such technology is still in its nascent stage and not widely used in field experiments, as with this battery-powered system small bat species cannot be tagged at present and it is not possible to follow many individuals over an extended period of time.

Our methodological contribution can be also adopted for other species where leading-following behavior plays a role and only recordings of individual positions are available. This includes, for example, automatic RFID-tag recordings at feeding stations34 and other resources where different group members meet, such as burrows in rodents35. As we demonstrate, such recordings can be systematically analyzed by comparing (statistically) the distributions of L/F time differences, to infer genuine L/F events.

A major contribution in addressing the first challenge is a thorough investigation of the parameters that allow to distinguish a L/F event from other types of encounters (e.g. local enhancement) at a given box. We recall that there is no ground truth available that informs us about the correct identification of L/F events from the data. We argued that the time differences of L/F events can be used to calibrate three relevant parameters: (1) the maximum allowed time difference (in minutes) between consecutive recordings of a leader and a follower, (2) the minimum time (in minutes) an experienced bat in an L/F event needs to potentially become a leader, i.e. the time needed to find and lead followers, and (3) the hour in the morning on the day of a box occupation, after which subsequent recordings from this box are ignored because occupation is considered to have already taken place.

Regarding the second challenge, we have proposed a new measure of individual influence that can be derived from the L/F networks we construct—second-degree centrality. Importantly, proxying influence as a centrality score goes beyond the widely used association indexes and the corresponding Mantel tests in studying animal systems. Association indexes are local measures in that they only reflect dyadic relations between any two individuals. To quantify the systemic influence of individuals, we need to provide measures that also capture their proclivity to act as social hubs, as recognized already by Farine and Whitehead34 and Brent36. In this respect, we see the application of social networks for analyzing information spreading as an excellent choice.

Our method differs also from common techniques34 to study animal association patterns via social networks. Typically, when social networks are used, the observed interaction strength between two individuals is either thresholded (i.e. it exists only if interaction strength is above threshold), sampled or used as a link weight, to calculate various association indexes17,37,38,39,40. In line with Farine and Whitehead34 (see specifically Fig. 2 therein), we do not threshold our networks to avoid dubious statistical biases. Instead, we include all of the observed individuals and their recorded activity and analyze the full scale of inferred interactions.

Note that we focus our analysis on dense networks (see Table 4). We found that these networks have only one weakly connected component (WCC) which contains most of the individuals. Density is a proxy for the intensity of leading-following behaviour, while the presence of one large WCC indicates that the majority of the colony partook in leading-following. Moreover, we also found that in most cases there are only very few (1-3) strongly connected components (SCC) of different size in the network (see Table 4).

Hence, we can conclude that individuals in the same SCC participated both as leaders and as followers in different events. This tells us that information about suitable roosts is not concentrated in only a few important individuals, but is spread across the whole colony.

At the same time, we could also detect that not all individuals play an equal role as leaders or followers. Instead, their influence, measured by leading inexperienced bats, differs considerably. To quantify these differences, we used different centrality measures as proxies of importance. Two of these, in-degree and eigenvector centrality, are established measures, while the third one, second-degree centrality is a new measure introduced by us. As explained in “Social Network Analysis” section, it provides a proper balance between direct and indirect influence in the information transfer.

Computing the different centralities for each individual, we could identify that there are only a few important individuals that lead most of the other bats. These individuals stand out regardless of the centrality measure used. In particular, we also calculated that there are significant correlations between the rankings obtained by using the different centrality measures. We emphasize that measuring influence by means of centralities cannot be simply reduced to comparing numbers of leading events. The latter would not allow us to distinguish whether individuals always lead the same or diverse followers, or whether such followers are of less or equal importance in comparison to the leader.

We believe that our results can guide future empirical and theoretical studies in two ways. First of all, we should realize that the constructed L/F networks do not already tell us about the mechanisms by which pairs of leaders and followers are formed. This process, known as recruitment, can be revealed by testing different recruitment rules in computer simulations, to check whether they result in the importance scores obtained from the empirical networks. In essence this entails the development of various null models. Null models are recognized as useful tools to test the viability of these recruitment rules in the presence of inherently non-independent behavioral data41. We investigate a variety of such null models about recruitment behavior in Bechstein’s bats in42.

Secondly, additional field work needs to be devoted to study the behavioural variability of individuals in playing their role as leaders or followers. For example, demographic, health or genetic characteristics can influence such roles43,44,45,46. With our study, however, we have already identified those individual bats that are prominent in these roles. This allows to target future experiments particularly toward individuals with very high or very low influence, to find out how different characteristics impact their leading-following behavior.