Population turnover facilitates cultural selection for efficiency in birds

Chimento, Michael; Alarcón‐Nieto, Gustavo; Aplin, Lucy M.

doi:10.1016/j.cub.2021.03.057

Cited by 37 publications

(60 citation statements)

References 41 publications

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“…However, a number of suppliers, including Loligo Systems, Cogniot, Eccel and Sparkfun, produce RFID HATs for the Raspberry Pi that can read ISO 11784 certified PIT tags for animal identification (at both 125 kHz and 134.2 kHz), and are already available for less than €1/tag. This offers a wide range of possible experimental manipulations, where unique individuals are automatically detected and in turn these detections can be programmed to trigger specific actions, including playback of audio or visual stimuli, controlling servos and providing commands to external devices (see Bolaños et al., 2017; Chimento et al., 2021; Noorshams et al., 2017; Silasi et al., 2018; Youngblood, 2020).…”

Section: Guidelines Pointers and Considerations For Implementing The Raspberry Pimentioning

confidence: 99%

“…Lertvilai (2020) built an automated recording device specifically for autonomous zooplankton surveys in the wild. And Hermann et al (2020) developed F I G U R E 3 A diverse range of Raspberry Pi applications: (a) plant phenotyping(Tausen et al, 2020), (b) high-throughput animal recordings(Jolles et al, 2017), (c) a dedicated plant growth cabinet(Ghosh et al, 2018), (d) a 3D-printed microscope (FlyPi; MaiaChagas et al, 2017), (e) an RFID-equipped birdfeeder(Youngblood, 2020), f) long-term monitoring of fish behaviour(Martorell-Barceló et al, 2021), (g) automated bird puzzle boxes(Chimento et al, 2021), (h) an autonomous underwater camera system(Lertvilai, 2020), (i) automated quantification of fly climbing behaviour(Spierer et al, 2021), (j) a field audio recorder for bioacoustics(Bradfer-Lawrence et al, 2019;Whytock & Christie, 2017), (k) an operant conditioning device for wild mesocarnivores(Stanton et al, 2020), (l) autonomous ecosystem monitoring(Sethi et al, 2018), (m) closed-loop virtual reality(Tadres & Louis, 2020), (n) long-term recording of fish from birth (K. Laskowski & J. Jolles, pers. comm.…”

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confidence: 99%

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Broad‐scale applications of the Raspberry Pi: A review and guide for biologists

Jolles

2021

Methods Ecol Evol

142

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1. The field of biology has seen tremendous technological progress in recent years, fuelled by the exponential growth in processing power and high-level computing, and the rise of global information sharing. Low-cost single-board computers are predicted to be one of the key technological advancements to further revolutionise this field.2. So far, an overview of current uptake of these devices and a general guide to help researchers integrate them in their work has been missing. In this paper I focus on the most widely used single-board computer, the Raspberry Pi, and review its broad applications and uses across the biological domain.3. Since its release in 2012, the Raspberry Pi has been increasingly taken up by biologists, in the laboratory, the field and in the classroom, and across a wide range of disciplines. A hugely diverse range of applications exists that ranges from simple solutions to dedicated custom-build devices, including nest-box monitoring, wildlife camera trapping, high-throughput behavioural recording, large-scale plant phenotyping, underwater video surveillance, closed-loop operant learning experiments and autonomous ecosystem monitoring. Despite the breadth of its implementations, the depth of uptake of the Raspberry Pi by the scientific community is still limited. 4. The broad capabilities of the Raspberry Pi, combined with its low cost, ease of use and large user community make it a great research tool for almost any project. To help accelerate the uptake of the Raspberry Pi by the scientific community, I provide detailed guidelines, recommendations and considerations, and 30+ step-by-step guides on a dedicated accompanying website (http://raspb erryp i-guide.github.io). I hope this paper will help generate more awareness about the Raspberry Pi among scientists and thereby both fuel the democratisation of science and ultimately help advance our understanding of biology, from the micro-to the macro-scale.

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Section: Guidelines Pointers and Considerations For Implementing The Raspberry Pimentioning

confidence: 99%

mentioning

confidence: 99%

Broad‐scale applications of the Raspberry Pi: A review and guide for biologists

Jolles

2021

Methods Ecol Evol

142

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“…This new tool-using technique apparently originated from widely present leaf-sponging behaviour [ 53 ], and further work showed that the new technique continued to spread through social learning, most likely because moss tools are faster to manufacture while offering increased absorption capacity [ 54 , 55 ]. Finally, a recent experimental study found a positive relationship between population turnover and the cultural evolution of efficiency in great tits [ 56 ]; knowledgeable residents more often innovated an efficient alternative foraging behaviour, which was then adopted by less experienced immigrants, so that population turnover greatly increased the probability that this efficient behaviour invaded populations. This result echoed an older study in blueheaded wrasse, in which naive individuals better re-sampled options for mating sites compared with experienced, resident fish [ 57 ].…”

Section: Evidence For Cultural Evolution In Animalsmentioning

confidence: 99%

“…[ 55 ] increasing reward (liquid absorption) organism great tit ( Parus major ) foraging puzzle latency to reward (food item) Chimento et al . [ 56 ] organism bighorn sheep ( Ovis canadensis ) moose ( Alces alces ) migration routes increasing reward (food availability via knowledge of phenology) Jesmer et al . [ 59 ] organism and behaviour chimpanzee ( Pan troglodytes ) extractive tool foraging increasing reward behavioural structure (bimanual coordination) Humle & Matsuzawa [ 68 ] organism and behaviour chimpanzee ( Pan troglodytes ) extractive tool foraging latency to reward behavioural structure (no.…”

Section: Defining and Quantifying Efficiencymentioning

confidence: 99%

“…In the case of mating-site preferences or bird song, benefits will probably be increased reproductive success [ 57 ]. Decreasing costs might come from reduced latency from the start of the behaviour to the reward [ 55 , 56 , 69 ], or decreased energy expenditure, measured, for example, in terms of oxygen consumption [ 70 ].…”

Section: Defining and Quantifying Efficiencymentioning

confidence: 99%

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Efficiency fosters cumulative culture across species

Gruber

Chimento

Aplin

et al. 2021

Phil. Trans. R. Soc. B

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Recent studies in several taxa have demonstrated that animal culture can evolve to become more efficient in various contexts ranging from tool use to route learning and migration. Under recent definitions, such increases in efficiency might satisfy the core criteria of cumulative cultural evolution (CCE). However, there is not yet a satisfying consensus on the precise definition of efficiency, CCE or the link between efficiency and more complex, extended forms of CCE considered uniquely human. To bring clarity to this wider discussion of CCE, we develop the concept of efficiency by (i) reviewing recent potential evidence for CCE in animals, and (ii) clarifying a useful definition of efficiency by synthesizing perspectives found within the literature, including animal studies and the wider iterated learning literature. Finally, (iii) we discuss what factors might impinge on the informational bottleneck of social transmission, and argue that this provides pressure for learnable behaviours across species. We conclude that framing CCE in terms of efficiency casts complexity in a new light, as learnable behaviours are a requirement for the evolution of complexity. Understanding how efficiency greases the ratchet of cumulative culture provides a better appreciation of how similar cultural evolution can be between taxonomically diverse species—a case for continuity across the animal kingdom. This article is part of a discussion meeting issue ‘The emergence of collective knowledge and cumulative culture in animals, humans and machines’.

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