Global cultural evolutionary model of humpback whale song

Funding: ECG is funded by a Royal Society University Research Fellowship (UF160081). RFL and LZ are funded by the BBSRC (BB/R008736/2). LL was supported by a Leverhulme Trust Grant to Luke Rendell (among other recipients; grant reference RPG-2013-367)Humpback whale song is an extraordinary example of vocal cultural behaviour. In northern popula-tions, the complex songs show long-lasting traditions that slowly evolve, while in the South Pacific, pe-riodic revolutions occur when songs are adopted from neighbouring populations and rapidly spread. In this species, vocal learning cannot be studied in the laboratory, learning is instead inferred from the songs’ complexity and patterns of transmission. Here, we used individual-based cultural evolutionary simulations of the entire Southern and Northern Hemisphere humpback whale populations to formalise this process of inference. We modelled processes of song mutation and patterns of contact among popu-lations and compared our model with patterns of song theme sharing measured in South Pacific popula-tions. Low levels of mutation in combination with rare population interactions were sufficient to closely fit the pattern of diversity in the South Pacific, including the distinctive pattern of West-to-East revolu-tions. Interestingly, the same learning parameters that gave rise to revolutions in the Southern Hemi-sphere simulations gave rise to evolutionary patterns of cultural evolution in the Northern Hemisphere populations. Our study demonstrates how cultural evolutionary approaches can be used to make infer-ences about the learning processes underlying cultural transmission and how they might generate emergent population-level processes.Publisher PDFPeer reviewe

Jackdaw nestlings can discriminate between conspecific calls but do not beg specifically to their parents

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Behavioral Ecology following peer review. The definitive publisher-authenticated version Lies Zandberg, Jolle W. Jolles, Neeltje J. Boogert, and Alex Thornton Jackdaw nestlings can discriminate between conspecific calls but do not beg specifically to their parents Behavioral Ecology (2014) 25 (3): 565-573 first published online February 28, 2014 doi:10.1093/beheco/aru026 is available online at: http://intl-beheco.oxfordjournals.org/content/25/3/565.The ability to recognize other individuals may provide substantial benefits to young birds, allowing them to target their begging efforts appropriately, follow caregivers after fledging, and establish social relationships later in life. Individual recognition using vocal cues is likely to play an important role in the social lives of birds such as corvids that provision their young postfledging and form stable social bonds, but the early development of vocal recognition has received little attention. We used playback experiments on jackdaws, a colonial corvid species, to test whether nestlings begin to recognize their parents’ calls before fledging. Although the food calls made by adults when provisioning nestlings were individually distinctive, nestlings did not beg preferentially to their parents’ calls. Ten-day-old nestlings not only responded equally to the calls of their parents, neighboring jackdaws whose calls they were likely to overhear regularly and unfamiliar jackdaws from distant nest boxes, but also to the calls of rooks, a sympatric corvid species. Responses to rooks declined substantially with age, but 20- and 28-day-old nestlings were still equally likely to produce vocal and postural begging responses to parental and nonparental calls. This is unlikely to be due to an inability to discriminate between calls, as older nestlings did respond more quickly and with greater vocal intensity to familiar calls, with some indication of discrimination between parents and neighbors. These results suggest that jackdaws develop the perceptual and cognitive resources to discriminate between conspecific calls before fledging but may not benefit from selective begging responses

Measuring mate preferences: Absolute and comparative evaluation of potential partners

Quantifying the direction and strength of mate preference is essential to improve our understanding of sexual selection. Experimental designs, however, often do not consider how individuals evaluate and compare the available options, which may affect the results significantly. Preferences are often assumed to be absolute, with individuals assigning a fixed, absolute value to a cue or potential partner they encounter. However, individuals may instead also compare the available options, in which case the social context plays an essential role in the preference for each potential partner. Here we investigated the importance of considering the choosers’ evaluation process in mate preference tests. Using a mate preference study on wild great tit, Parus major, heterozygosity, breast stripe size and yellowness as a case study, we tested whether individuals use absolute or comparative mate preferences. We analysed how the perceived average attractiveness and the variation in attractiveness of the group of potential mates affected the measured preference functions. We found that the average attractiveness of the stimulus groups affected the total time individuals spent visiting all stimulus birds, and that the variation in attractiveness within groups affected the measured preference slopes. This indicates that the social context will affect the measured responses to stimulus groups, and that great tits may use both absolute and comparative evaluation. Considering how a study species encounters and evaluates potential mates and how the social environment may affect preferences is essential when choosing an appropriate experimental design to obtain reliable measurements of mate preferences. We therefore strongly advise future studies to consider not only the absolute stimulus trait values, but also the context in which they are presented. The ability to quantify preferences accurately will increase our understanding of mate preferences, mate choice and ultimately sexual selection

Personality-dependent differences in problem-solving performance in a social context reflect foraging strategies

Individuals develop innovative behaviours to solve foraging challenges in the face of changing environmental conditions. Little is known about how individuals differ in their tendency to solve problems and in their subsequent use of this solving behaviour in social contexts. Here we investigated whether individual variation in problem-solving performance could be explained by differences in the likelihood of solving the task, or if they reflect differences in foraging strategy. We tested this by studying the use of a novel foraging skill in groups of great tits (Parus major), consisting of three naive individuals with different personality, and one knowledgeable tutor. We presented them with multiple, identical foraging devices over eight trials. Though birds of different personality type did not differ in solving latency; fast and slow explorers showed a steeper increase over time in their solving rate, compared to intermediate explorers. Despite equal solving potential, personality influenced the subsequent use of the skill, as well as the pay-off received from solving. Thus, variation in the tendency to solve the task reflected differences in foraging strategy among individuals linked to their personality. These results emphasize the importance of considering the social context to fully understand the implications of learning novel skills

Bird song comparison using deep learning trained from avian perceptual judgments

Our understanding of bird song, a model system for animal communication and the neurobiology of learning, depends critically on making reliable, validated comparisons between the complex multidimensional syllables that are used in songs. However, most assessments of song similarity are based on human inspection of spectrograms, or computational methods developed from human intuitions. Using a novel automated operant conditioning system, we collected a large corpus of zebra finches’ (Taeniopygia guttata) decisions about song syllable similarity. We use this dataset to compare and externally validate similarity algorithms in widely-used publicly available software (Raven, Sound Analysis Pro, Luscinia). Although these methods all perform better than chance, they do not closely emulate the avian assessments. We then introduce a novel deep learning method that can produce perceptual similarity judgements trained on such avian decisions. We find that this new method outperforms the established methods in accuracy and more closely approaches the avian assessments. Inconsistent (hence ambiguous) decisions are a common occurrence in animal behavioural data; we show that a modification of the deep learning training that accommodates these leads to the strongest performance. We argue this approach is the best way to validate methods to compare song similarity, that our dataset can be used to validate novel methods, and that the general approach can easily be extended to other species

Machine Learning for Bird Song Learning (ML4BL) dataset

General description This dataset contains Zebra Finch decisions about perceptual similarity on song units. All the data and files are used for reproducing the results of the paper \u27Bird song comparison using deep learning trained from avian perceptual judgments\u27 by the same authors. Git repo on Zenodo: https://doi.org/10.5281/zenodo.5545932 Git repo access: https://github.com/veronicamorfi/ml4bl/tree/v1.0.0 Directory organisation: ML4BL_ZF |_files |_Final_probes_20200816.csv - all trials and decisions of the birds (aviary 1 cycle 1 data are removed from experiments) |_luscinia_triplets_filtered.csv - triplets to use for training |_mean_std_luscinia_pretraining.pckl - mean and std of luscinia triplets used for trianing |_*_cons_* - % side consistency on triplets (train/test) - train set contains both train and val splits |_*_gt_* - cycle accuracy for triplets of the specific bird (train/test) - train set contains both train and val splits |_*_trials_* - number of decisions made for a triplet (train/test) - train set contains both train and val splits |_*_triplets_* - triplet information (aviary_cycle-acc_birdID, POS, NEG, ANC) (train/test) - train set contains both train and val splits |_*_low*_ - low-margin (ambiguous) triplets (train/val/test) |_*_high_ - high-margin (unambiguous) triplets (train/val/test) |_*_cycle_bird_keys_* - unique aviary_cycle-acc_birdID keys (train/test) - train set contains both train and val splits |_TunedLusciniaV1e.csv - pairwise distance of two recordings computed by Luscinia |_training_setup_1_ordered_acc_single_cons_50_70_trials.pckl - dictionary containing everything needed for training the model (keys: \u27train_keys\u27, \u27train_triplets\u27, \u27val_keys\u27, \u27vali_triplets\u27, \u27test_triplets\u27, \u27test_keys\u27, \u27train_mean\u27, \u27train_std\u27) |_melspecs - *.pckl - melspectrograms of recordings |_wavs - *wav - recordings |_README.txt Recordings 887 syllables extracted from zebra finch song recordings, with a sampling rate of 48kHz and high pass filtered (100Hz), with a 20ms intro/outro fade. Decisions Triplets were created from the recordings and the birds made side based decisions about their similarity (see \u27Bird song comparison using deep learning trained from avian perceptual judgments\u27 for further information). Training dictionary Information Dictionary keys: \u27train_keys\u27, \u27train_triplets\u27, \u27val_keys\u27, \u27vali_triplets\u27, \u27test_triplets\u27, \u27test_keys\u27, \u27train_mean\u27, \u27train_std\u27 train_triplets/vali_triplets/test_triplets: Aviary_Cycle_birdID, POS, NEG, ANC, Decisions, Cycle_ACC(%), Consistency(%) train_keys/val_keys/test_keys: Aviary_Cycle_birdID train_mean/train_std: shape: (1, mel_bins) Open Access This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. Contact info Please send any questions about the recordings to: Lies Zandberg: [email protected] Please send any feedback or questions about the code and the rest of the data to: Veronica Morfi: [email protected]

Data from: Direct fitness benefits explain mate preference, but not choice, for similarity in heterozygosity levels, Ecology Letters

Data from: <b><i>Direct fitness benefits explain mate preference, but not choice, for similarity in heterozygosity levels - </i></b><i>Lies Zandberg, Gerrit Gort, Kees van Oers, Camilla A. Hinde</i><p><i></i></p><div><i>Ecology Letters</i></div