Biology subjects

Alarcon-Nieto, G.

Publications and source records attributed to Alarcon-Nieto, G..

4 recordsLinked to original sources

bioRxiv2024

Immigrant birds use payoff biased social learning in spatially variable environments

Longstanding theory predicts that strategic flexibility in when and how to use social information can help individuals make adaptive decisions, especially when environments are temporally or spatially variable. A short-term increase in reliance on social information under these conditions has been experimentally shown in primates, including humans, but whether this occurs in other taxa is unknown. We asked whether migration between spatially variable environments affected social information use with a large-scale cultural diffusion experiment with wild great tits (Parus major) in captivity, a small passerine bird that can socially learn novel behaviors. We simulated an immigration event where knowledgeable birds were exchanged between groups with opposing preferences for a socially learned foraging puzzle, living in similar or different environments. We found evidence that both immigrants and residents were influenced by social information and attended to the rewards that others received. Our analysis supported the use of a payoff-biased social learning by immigrants when both resources and habitat features were spatially variable. In contrast, immigrants relied more-so on individual learning when payoffs or the environment were unchanged. In summary, our results suggest that great tits assess the payoffs others receive, and are more influenced by socially observed differences in payoffs when environmental cues differ in their new environment. Our results provide experimental support for the hypothesis that spatial variability is a strong driver for the evolution of social learning strategies.

animal behavior and cognition↗

bioRxiv2023

callsync: an R package for alignment and analysis of multi-microphone animal recordings

O_LITo better understand how vocalisations are used during interactions of multiple individuals, studies are increasingly deploying on-board devices with a microphone on each animal. The resulting recordings are extremely challenging to analyse, since microphone clocks drift non-linearly and record the vocalisations of non-focal individuals as well as noise. C_LIO_LIHere we address this issue with callsync, an R package designed to align recordings, detect and assign vocalisations to the caller, trace the fundamental frequency, filter out noise and perform basic analysis on the resulting clips. C_LIO_LIWe present a case study where the pipeline is used on a dataset of six captive cockatiels (Nymphicus hollandicus) wearing backpack microphones. Recordings initially had drift of [~]2 minutes, but were aligned to within [~]2 seconds with our package. Using callsync, we detected and assigned 2101 calls across three multi-hour recording sessions. Two had loud beep markers in the background designed to help the manual alignment process. One contained no obvious markers, in order to demonstrate that markers were not necessary to obtain optimal alignment. We then used a function that traces the fundamental frequency and applied spectrographic cross correlation to show a possible analytical pipeline where vocal similarity is visually assessed. C_LIO_LIThe callsync package can be used to go from raw recordings to a clean dataset of features. The package is designed to be modular and allows users to replace functions as they wish. We also discuss the challenges that might be faced in each step and how the available literature can provide alternatives for each step. C_LI

ecology↗

bioRxiv2021

Machine learning reveals cryptic dialects that guide mate choice in a songbird

Culturally transmitted communication signals - such as human language or bird song - can change over time through a process of cultural drift, and may consequently enhance the separation of populations, potentially leading to reproductive isolation1-4. Local song dialects have been identified in bird species with relatively simple songs where individuals show high cultural conformity5-10. In contrast, the emergence of cultural dialects has been regarded as unlikely11-13 for species with more variable song, such as the zebra finch (Taeniopygia guttata). Instead, it has been proposed that selection for individual recognition and distinctiveness may lead to a complete spread across the space of acoustic and syntactical possibilities11-15. However, another possibility is that analytical limitations have meant that subtle but possibly salient group differences have not yet been discovered in such species. Here we show that machine learning can distinguish the songs from multiple captive zebra finch populations with remarkable precision, and that these cryptic song dialects drive strong assortative mating in this species. We studied mating patterns across three consecutive generations using captive populations that have evolved in isolation for about 100 generations. Cross-fostering eggs within and between these populations and quantifying social interactions of the resulting offspring later in life revealed that mate choice primarily targets cultural traits that are transmitted during a short developmental time window. Detailed social networks showed that females preferentially approached males whose song resembled that of their adolescent peers. Our study shows that birds can be surprisingly sensitive to cultural traits for mating that have hitherto remained cryptic, even in this well-studied species that is used as a model for song-learning13,14,16-28.

animal behavior and cognition↗

bioRxiv2017

An automated barcode tracking system for behavioural studies in birds

O_LIRecent advances in technology allow researchers to automate the measurement of animal behaviour. These methods have multiple advantages over direct observations and manual data input as they reduce bias related to human perception and fatigue, and deliver more extensive and complete data sets that enhance statistical power. One major challenge that automation can overcome is the observation of many individuals at once, enabling whole-group or whole-population tracking.\nC_LIO_LIWe provide a detailed description for implementing an automated system for tracking birds. Our system uses printed, machine-readable codes mounted on backpacks. This simple, yet robust, tagging system can be used simultaneously on multiple individuals to provide data on bird identity, position and directionality. Further, because our codes and backpacks are printed on paper, they are very lightweight.\nC_LIO_LIWe describe the implementation of this automated system on two flocks of zebra finches. We test different camera options, and describe their advantages and disadvantages. We show that our method is reliable, relatively easy to implement and monitor, and with proper handling, has proved to be safe for the birds over long periods of time. Further, we highlight how using single-board computers to control the frequency and duration of image capture makes this system affordable, flexible, and adaptable to a range of study systems.\nC_LIO_LIThe ability to automate the measurement of individual positions has the potential to significantly increase the power of both observational and experimental studies. The system can capture both detailed interactions (using video recordings) and repeated observations (e.g. once per second for the entire day) of individuals over long timescales (months or potentially years). This approach opens the door to tracking life-long relationships among individuals, while also capturing fine-scale differences in behaviour.\nC_LI

animal behavior and cognition↗