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

Wang, Daiping; Forstmeier, Wolfgang; Farine, Dan; Maldonado-Chaparro, Adriana Alexandra; Martin, Katrin; Pei, Yifan; Alarcón‐Nieto, Gustavo; Klarevas‐Irby, James A.; Ma, Shouwen; Aplin, Lucy M.; Kempenaers, Bart

doi:10.1038/s41467-022-28881-w

Cited by 14 publications

(11 citation statements)

References 65 publications

(76 reference statements)

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“…We randomly re-allocated the trait value across the nodes in the network 9999 times and calculated the assortativity coefficient for each permutated network to test significance. The p -value is equivalent to the proportion of assortativity coefficients that were larger than the observed coefficient [71].…”

Section: Methodsmentioning

confidence: 99%

Mixed-species bird flocks re-assemble interspecific associations across an elevational gradient

et al. 2022

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Understanding how non-trophic social systems respond to environmental gradients is still a challenge in animal ecology, particularly in comparing changes in species composition to changes in interspecific interactions. Here, we combined long-term monitoring of mixed-species bird flocks, data on participating species' evolutionary history and traits, to test how elevation affected community assemblages and interspecific interactions in flock social networks. Elevation primarily affected flocks through reassembling interspecific associations rather than modifying community assemblages. Specifically, flock networks at higher elevations (compared to low elevations) had stronger interspecific associations (larger average weighted degree), network connectivity (enhanced network density) and fewer subnetworks. A phylogenetic and functional perspective revealed that associations between similar species weakened, whereas connections between dissimilar and/or random species were unchanged or strengthened with elevation. Likewise, network assortativity for the traits of vertical stratum and breeding period declined with elevation. The overall pattern is a change from modular networks in the lowlands, where species join flocks with other species that have matching traits, to a more open, random system at high elevations. Collectively, this rewiring of interspecific networks across elevational gradients imparts network stability and resiliency and makes mixed-species flocks less sensitive to local extinctions caused by harsh environments.

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Section: Methodsmentioning

confidence: 99%

Mixed-species bird flocks re-assemble interspecific associations across an elevational gradient

et al. 2022

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“…Although these isolated syllables, rather than full song, may have sounded less natural to the birds, and also limited the possibilities of measuring higher levels of song perceptions, they made it possible to study sound perception using sounds with features they are naturally attuned to. Furthermore, although all syllables used originated from a single population (di↵erent from the experimental population), since the variation in syllables within a zebra finch colony is large compared to that between colonies (Lachlan et al, 2016) (but see Wang et al (2022)), we do not expect that this limits the generalisability of the algorithm for di↵erent zebra finch colonies. Finally, the current algorithm is only based on zebra finch perceptual judgements which may not be the same for all bird species.…”

Section: Discussionmentioning

confidence: 99%

Bird song comparison using deep learning trained from avian perceptual judgments

Zandberg

Morfi

George

et al. 2022

Preprint

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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.

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“…While integrating over time is a challenge for many fields (e.g. mate choice [ 11 ], dominance [ 12 ], social learning [ 13 ]), here we argue that it also merits attention in the field of collective behaviour. This is because collective actions emerge from interactions among many individuals, which can mask the distinct experience-driven preferences of each contributor.…”

Section: Introductionmentioning

confidence: 99%

Challenges of mismatching timescales in longitudinal studies of collective behaviour

Ogino

Strauss

Farine

2023

Phil. Trans. R. Soc. B

Self Cite

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How individuals’ prior experience and population evolutionary history shape emergent patterns in animal collectives remains a major gap in the study of collective behaviour. One reason for this is that the processes that can shape individual contributions to collective actions can happen over very different timescales from each other and from the collective actions themselves, resulting in mismatched timescales. For example, a preference to move towards a specific patch might arise from phenotype, memory or physiological state. Although providing critical context to collective actions, bridging different timescales remains conceptually and methodologically challenging. Here, we briefly outline some of these challenges, and discuss existing approaches that have already generated insights into the factors shaping individual contributions in animal collectives. We then explore a case study of mismatching timescales—defining relevant group membership—by combining fine-scaled GPS tracking data and daily field census data from a wild population of vulturine guineafowl ( Acryllium vulturinum ). We show that applying different temporal definitions can produce different assignments of individuals into groups. These assignments can then have consequences when determining individuals' social history, and thus the conclusions we might draw on the impacts of the social environment on collective actions. This article is part of a discussion meeting issue ‘Collective behaviour through time’.

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

Cited by 14 publications

References 65 publications

Mixed-species bird flocks re-assemble interspecific associations across an elevational gradient

Mixed-species bird flocks re-assemble interspecific associations across an elevational gradient

Bird song comparison using deep learning trained from avian perceptual judgments

Challenges of mismatching timescales in longitudinal studies of collective behaviour

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