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Raspberry Pi nest cameras: An affordable tool for remote behavioral and conservation monitoring of bird nests

ABSTRACT: Abstract Bespoke (custom‐built) Raspberry Pi cameras are increasingly popular research tools in the fields of behavioral ecology and conservation, because of their comparative flexibility in programmable settings, ability to be paired with other sensors, and because they are typically cheaper than commercially built models. Here, we describe a novel, Raspberry Pi‐based camera system that is fully portable and yet weatherproof—especially to humidity and salt spray. The camera was paired with a passive infrared sensor, to create a movement‐triggered camera capable of recording videos over a 24‐hr period. We describe an example deployment involving “retro‐fitting” these cameras into artificial nest boxes on Praia Islet, Azores archipelago, Portugal, to monitor the behaviors and interspecific interactions of two sympatric species of storm‐petrel (Monteiro's storm‐petrel Hydrobates monteiroi and Madeiran storm‐petrel Hydrobates castro) during their respective breeding seasons. Of the 138 deployments, 70% of all deployments were deemed to be “Successful” (Successful was defined as continuous footage being recorded for more than one hour without an interruption), which equated to 87% of the individual 30‐s videos. The bespoke cameras proved to be easily portable between 54 different nests and reasonably weatherproof (~14% of deployments classed as “Partial” or “Failure” deployments were specifically due to the weather/humidity), and we make further trouble‐shooting suggestions to mitigate additional weather‐related failures. Here, we have shown that this system is fully portable and capable of coping with salt spray and humidity, and consequently, the camera‐build methods and scripts could be applied easily to many different species that also utilize cavities, burrows, and artificial nests, and can potentially be adapted for other wildlife monitoring situations to provide novel insights into species‐specific daily cycles of behaviors and interspecies interactions. We present a methodological paper focusing on a practical issue, describing a novel and easily constructed field camera system that has been designed (and rigorously tested) for deployment in remote locations with harsh conditions. We describe how the system can be easily adapted for diverse conservation/management and field ecology projects.

SUBMITTER: Hereward H

PROVIDER: S-EPMC8571635 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPlant science uses increasing amounts of phenotypic data to unravel the complex interactions between biological systems and their variable environments. Originally, phenotyping approaches were limited by manual, often destructive operations, causing large errors. Plant imaging emerged as a viable alternative allowing non-invasive and automated data acquisition. Several procedures based on image analysis were developed to monitor leaf growth as a major phenotyping target. However, in most proposals, a time-consuming parameterization of the analysis pipeline is required to handle variable conditions between images, particularly in the field due to unstable light and interferences with soil surface or weeds. To cope with these difficulties, we developed a low-cost, 2D imaging method, hereafter called PYM. The method is based on plant leaf ability to absorb blue light while reflecting infrared wavelengths. PYM consists of a Raspberry Pi computer equipped with an infrared camera and a blue filter and is associated with scripts that compute projected leaf area. This new method was tested on diverse species placed in contrasting conditions. Application to field conditions was evaluated on lettuces grown under photovoltaic panels. The objective was to look for possible acclimation of leaf expansion under photovoltaic panels to optimise the use of solar radiation per unit soil area.ResultsThe new PYM device proved to be efficient and accurate for screening leaf area of various species in wide ranges of environments. In the most challenging conditions that we tested, error on plant leaf area was reduced to 5% using PYM compared to 100% when using a recently published method. A high-throughput phenotyping cart, holding 6 chained PYM devices, was designed to capture up to 2000 pictures of field-grown lettuce plants in less than 2 h. Automated analysis of image stacks of individual plants over their growth cycles revealed unexpected differences in leaf expansion rate between lettuces rows depending on their position below or between the photovoltaic panels.ConclusionsThe imaging device described here has several benefits, such as affordability, low cost, reliability and flexibility for online analysis and storage. It should be easily appropriated and customized to meet the needs of various users.

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Raspberry Pi nest cameras: An affordable tool for remote behavioral and conservation monitoring of bird nests

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