Programmable Automated System for Songbird Ecobehavioral Research (PASSER): Using flexible computer‐integrated feeders to conduct high resolution studies of environment–behavior dynamics in songbirds

Philson, Conner S.; Ray, Andrew J.; Foltz, Sarah L.; Davis, Jason

doi:10.1002/ece3.4638

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…comm. January 2021), (o) long‐term monitoring of captive bird flocks (Alarcón‐Nieto et al., 2018), (p) an operant licking device (Longley et al., 2017), (q) autonomous motor‐controlled video recording (Heuschele et al., 2020), (r) a smart birdfeeder to collect behavioural and environmental data (Philson et al., 2018), (s) high‐throughput ethomics (Geissmann et al., 2017), (t) real‐time measurements of mouse activity, body temperature and respiratory rate (Kallmyer et al., 2019) and (u) measurement of respiration of nest‐building ants (A. Rathery, pers. comm.…”

Section: Overview Of Applications Across the Biological Domainmentioning

confidence: 99%

“…Philson et al. (2018) used the Raspberry Pi to build an automated bird feeder that dispenses food using a servo‐controller when a proximity sensor detects a bird. Finally, M. Oellermann (pers.…”

Section: Overview Of Applications Across the Biological Domainmentioning

confidence: 99%

“…McBride and Courter (2019) and Philson et al. (2018) automatically acquired temperature, wind speed and humidity of the micro‐climates around birdfeeders in the field, and, using a manual sensor connected to a Raspberry Pi, Bardunias et al. (2020) made use of a Raspberry Pi to measure the humidity of termite mounds in the Namibian desert.…”

Section: Overview Of Applications Across the Biological Domainmentioning

confidence: 99%

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

Jolles

2021

Methods Ecol Evol

152

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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: Overview Of Applications Across the Biological Domainmentioning

confidence: 99%

Section: Overview Of Applications Across the Biological Domainmentioning

confidence: 99%

Section: Overview Of Applications Across the Biological Domainmentioning

confidence: 99%

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

Jolles

2021

Methods Ecol Evol

152

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Do hihi lose access to supplemental feeders because of the presence of korimako?

Roper,

Brunton

2023

New Zealand Journal of Zoology

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Using Machine Learning to Identify Associations between the Environment, Occurrence, and Outcomes of Songbird Displacements at Supplemental Feeders

Philson

Pelletier

Foltz

et al. 2022

Birds

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The context and outcome of aggressive interactions between individuals has important fitness consequences. Displacements—an aggressive interaction wherein one individual is chased from a location by another—also have implications for social hierarchy formation and geographic distribution in songbirds. Morphological correlates, like body size, and social correlates, such as dominance rank, have been shown to mediate displacements in songbirds. However, the role of the physical environment, namely temperature, humidity, and time of day, which may influence an individual’s energy needs and thus displacement motivation, has remained understudied. We monitored songbird feeding and displacement behaviors using computerized automated feeders. We observed asymmetric differences across species in displacement involvement. To identify the conditions of the social and physical environment that are associated with the occurrence and outcome of songbird displacements at supplemental feeders, we use the machine learning approach, random forest, which is a novel method to the fields of ornithology and animal behavior. From our random forest models, we found that the attributes of the physical environment (i.e., humidity and the time of day) are associated with the occurrence of a displacement event, whereas the attributes of the social environment (i.e., species of the displacer and displaced individuals) are associated with which species are involved. These results provide context to develop further observational and experimental hypotheses to tease apart the inner workings of these multifactorial behaviors on a larger scale and provide a proof of concept for our analytical methods in the study of avian behavior.

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Programmable Automated System for Songbird Ecobehavioral Research (PASSER): Using flexible computer‐integrated feeders to conduct high resolution studies of environment–behavior dynamics in songbirds

Cited by 3 publications

References 29 publications

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

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

Do hihi lose access to supplemental feeders because of the presence of korimako?

Using Machine Learning to Identify Associations between the Environment, Occurrence, and Outcomes of Songbird Displacements at Supplemental Feeders

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