SR: Communication Flashcards
Deception by Flexible Alarm Mimicry in an African Bird
Tom P. Flower,1,2* Matthew Gribble,2 Amanda R. Ridley 2014
Deception is common in nature, but victims of deception discriminate against and ultimately ignore deceptive signals when they are produced too frequently. Flexible variation of signals could allow evasion of such constraints. Fork-tailed drongos (Dicrurus adsimilis) use false alarm calls
to scare other species away from food that they then steal.
We show that drongos mimic the alarms of targeted species. Further, target species reduce their response to false alarm calls when they are repeated. However, the fear response is maintained when the call is varied. Drongos
exploit this propensity by changing their alarm-call type when making repeated theft attempts on a particular species. Our results show that drongos can evade the frequency-dependent constraints that typically limit deception payoffs through flexible variation of their alarm calls.
Drongos produce a diversity of alarm call types (11). Individual repertoires range from 9 to 32 different calls (mean T SE = 17 T 1), and a total of 51 different alarm call types have been recorded in false alarms (11). Six calls are drongo-specific, but 45 mimic other speciesʼ alarm calls, including those of target species (11).
Highlighting the im- portance of alarm mimicry, drongos exclusively produce mimicked alarm calls in 42% of false alarms and combine mimicked and drongo-specific alarm calls in a further 27% of false alarms (11). However, the specific benefit drongos gain from producing such a large array of mimetic alarm calls is unclear.
Although these results indicate that alarm mim- icry by drongos increases target deception, targets are still predicted to habituate to repeated use of the same false alarm call regardless of its type (5). We used a second experiment to test our hypothesis that, by flexibly varying their false alarm calls during repeated food-theft attempts, drongos main- tain the intensity of target responses.
We played four treatments of three alarm calls to individual babblers (n = 22 babblers in 11 groups) pro- visioned with a food item and measured their response time (3 days between treatments and 20 min between calls). Of the four treatments, two contained three alarm calls of the same type, either drongo-specific or mimicked starling, whereas in the other two treatments the third call was changed to the opposite type, either drongo-specific to mim- icked starling or vice versa (Fig. 3A) (7). Babblers decreased their response when the same alarm call type was played three times in succession but maintained their response when the third alarm was changed (Fig. 3B and table S3). Furthermore, the duration of their response to the third alarm was greater when it was changed relative to when it was kept the same
Drongos thus could benefit by flexibly varying their call type to maintain target deception. We tested whether drongos exploit this possibility under natural circumstances by changing their false alarm calls during repeated food-theft attempts on a target and, more specifically, whether they change their call type when their previous food-theft attempt failed.
We observed repeated food-theft attempts by drongos on the same target on 151 occasions (n = 42 drongos) and found that they changed their alarm call type on 74 T 6% of occasions (7). In particular, drongos were more likely to change the type of false alarm call when their previous food-theft attempt failed (Fig. 4A and table S4). Furthermore, when drongos changed their false alarm-call type after a failed food-theft attempt, they were more likely to successfully steal food than when they made the same alarm call (Fig. 4B and table S5).
Our results suggest that, by flexibly varying their deceptive signal, drongos benefit in two ways. First, they produce signals more likely to deceive their targets. Second, they avoid target habituation to repeated use of the same deceptive signal and thereby evade the frequency-dependent constraints that typically limit payoffs from decep- tive communication. Such benefits are analogous to those provided by antigenic variation, whereby infectious organisms, including those responsible for influenza, sleeping sickness (Trypanosomiasis brucei), and malaria (Plasmodium falciparum), vary cell- surface proteins to evade host immune responses (19).
Our results suggest a deceptive function for vocal mimicry, a behavior for which few adaptive benefits have been demonstrated (23). Further, we found that drongos specifically change their alarm calls to both mimic targets and exploit feed- back from target alarm responses, thereby increasing their success.
This shows that attending to feedback in deceptive communication may be adaptive, com- plementing recent research on feedback in other communication systems where individuals repeat- edly interact (21, 24, 25). Such deceptive flexibility and the drongosʼ production of both honest and dishonest alarms, termed tactical deception (26), are considered evidence that species possess cog- nitive abilities, including mental state attribution, akin to theory of mind (26– 28).
However, the evolved mechanisms responsible for similar behavior in different species are not necessarily the same and likely vary with speciesʼ ecology. Determining what different mechanisms enable the production of complex behavior and when these are selectively advantageous remain key questions in evolutionary biology.
Reciprocal signaling in honeyguide-human mutualism
Claire N. Spottiswoode,1,2* Keith S. Begg,3 Colleen M. Begg 2016
Greater honeyguides (Indicator indicator) lead human honey-hunters to wild beesʼ nests, in a rare example of a mutualistic foraging partnership between humans and free-living wild animals. We show experimentally that a specialized vocal sound made by Mozambican honey-hunters seeking beesʼ nests elicits elevated cooperative behavior from honeyguides.
The production of this sound increased the probability of being guided by a honeyguide from about 33 to 66% and
the overall probability of thus finding a beesʼ nest from 17 to 54%, as compared with other animal or human sounds of similar amplitude. These results provide experimental evidence that a wild animal in a natural setting responds adaptively to a human signal of cooperation.
First, we confirmed that in northern Mozambique, honeyguides give reliable information to human honey-hunters. To test whether guiding behavior accurately indicates the direction of beesʼ nests and leads to their successful discovery by humans, we trailed honey-hunters following honeyguides and tracked our movements via GPS. A guiding event was defined as a bout of guiding by an individual bird, sometimes involving consecu- tive journeys to different beesʼ nests.
Each guid- ing event probably involved a different individual honeyguide, as the study area was 230 km2, and the home ranges of individual honeyguides that we measured using radio telemetry did not ex- ceed 1 km2 and overlapped with one another (7) (fig. S1). 75.3% of guiding events led to the suc- cessful discovery by humans of at least one beesʼ nest [mean ± SE = 1.00 ± 0.08 nests; range = 0 to 3 nests; n = 97 events, excluding controls in the experiment discussed below (7)]. 94.6% of nests shown belonged to the honeybee Apis mellifera, and the rest to stingless bee species (7).
Second, we asked whether the signals used by human honey-hunters provide reliable information to honeyguides. Honey-hunters seeking honeyguides often announce their presence with unspecialized sounds such as shouting and chopping wood (4, 8). In some parts of Africa, however, humans also make specialized vocalizations used only when hunting honey.
In the Niassa National Reserve (and, more widely, in northern Mozambique and adjacent southern Tanzania), Yao honey-hunters seeking and following honeyguides produce a loud trill followed by a grunt: “brrrr-hm” [audio S1; see (9) for a me- lodious whistle used in the same context by the Hadza people of northern Tanzania]. To confirm that “brrrr-hm” is a specialized honey-hunting sound, we interviewed 20 Yao honey-hunters, all of whom reported that they used this specific sound when hunting honey but in no other context. they reported that they learned it from their fathers and that it is the best way of attracting a honeyguide and
maintaining its attention.
honeyguides should be more likely to initiate collaboration with humans producing this honey-hunting sound rather than other sounds. To test this, we carried out 72 15-min experimental transects simulating honey- hunting forays, in which an author and two local honey-hunters walked while playing back one of three acoustic cues every 7 s at consistent ampli- tude using a calibrated speaker: (i) a control hu- man sound (either the Yao words for “honeyguide” and “honey” or the honey-hunterʼs name, alternated among transects); (ii) a control animal sound (either the song or the excitement call of the ring-necked dove, Streptopelia capicola, alternated among tran-
sects); or (iii) the specialized “brrrr-hm” honey- hunting sound
We were guided by a honeyguide on 30 of 72 transects. Transects accompanied by the honey- hunting call had a 66.7% probability of eliciting guiding from a honeyguide, which was significantly greater than that for transects accompanied by the human control sounds (25%) or animal control sounds (33.3%) (Fig. 2A; planned comparison with controls: estimate ± SE = 1.13 ± 0.38, Z = 2.96, P = 0.0031). The probability of guiding did not differ between the two control treatments (estimate ± SE = 0.25 ± 0.33, Z = 0.76, P = 0.45). The best model also included the time relative to sunrise or sun- set as a covariate (probability of being guided weakly decreased closer to the middle of the day: estimate ± SE = –4.34 ± 0.20, Z = –2.13, P = 0.034) and, overall, explained 25% of the variance in probability of being led by a honeyguide.
Overall, the honey-hunting sound resulted in a 54.2% pre- dicted probability of finding a beesʼ nest (Fig. 2B; planned comparison with controls: estimate ± SE = 1.21 ± 0.39, Z = 3.14, P = 0.0017) compared with 16.7% for each of the control sounds (planned comparison between controls: estimate ± SE = 0.03 ± 0.39, Z = 0.08, P = 0.94).
tested whether the amplitudes explained any variance in guiding behavior, ei- ther in isolation or in the multivariate models above. In no case did these acoustic measures explain any variance in the probability of being guided or being shown a beesʼ nest (7). There- fore, the honeyguidesʼ elevated response to the honey-hunting sound is unlikely to be explained by its audibility. Instead, the most parsimonious explanation is that honeyguides associate the honey- hunting sound with successful collabo- ration. Such partner choice should be adaptive by allowing honeyguides to improve their net benefit from interacting with humans.
Honey- guides are brood-parasitic and reared by insectivo- rous hosts (4), which suggests that their propensity to locate beesʼ nests and guide humans to them is likely to be innate. However, the “brrrr-hm” human signal studied here is confined to a specific geo- graphical area, and a different cultural group
living 1000 km away uses a different signal which is likely to have the same function (9). Local adaptation is unlikely to account for corresponding honeyguide specialization, given a lack of obvious genetic struc- ture across its range (13).
This implies that local refinements to guiding behavior are probably learned, which is supported anecdotally by the belief of many Yao honey- hunters that juvenile honeyguides [which have distinctive yellow plu- mage (4)] are a separate species (called “naman- dindi”) that, despite beckoning humans in the manner of an adult honeyguide (“sego”), falls quiet in response to the honey-hunting sound. We pro- pose that learning might occur socially from con- specifics in the vicinity of beesʼ nests, resulting in a local cultural tradition among honeyguides that reflects the customs of their human collaboration
