Methodological Considerations for Monitoring Wild Bird Nests Using Video Technology

McQuillen, Harry; Brewer, Larry W.

doi:10.1648/0273-8570-71.1.167

Cited by 28 publications

(27 citation statements)

References 6 publications

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“…The maintenance/monitoring frequencies reported in the articles we reviewed varied widely. Frequencies were wide ranging with daily (Hanula and others, 2000;Langston and others, 2007;Marnewick and others, 2008;McQuillen and Brewer, 2000;Mills and others, 2005;Pietz and Granfors, 2000;Thompson and Burhans, 2003), every 2-3 days (King and others, 2001;Rogers and others, 2005;Steen, 2009;Stein and others, 2008), once a week (Donaldson, 2007;Larrucea and others, 2007), every two weeks (Grivas and others, 2009;Karhu and Anderson, 2006;O'Brien and others, 2006), and once a month (Song and others, 2008) maintenance reported. Maintenance frequencies seemed tied to the type of technology used.…”

Section: System Maintenance and Monitoringmentioning

confidence: 99%

“…Miniature video cameras were used in some studies of nesting behavior (Lewis and others, 2004;McQuillen and Brewer, 2000) and nest predation (Pietz and Granfors, 2000;Renfrew and Ribic, 2003;Staller and others, 2005;Thompson and Burhans, 2003). Video cameras have been incorporated into animal-borne camera systems (cameras temporarily attached to the animals) to study animal behaviors, including resource use and interactions with other animals (Beringer and others, 2004;Bluff and Rutz, 2008;Heithaus, Dill, and others, 2002;Heithaus, McLash, and others, 2002;Hooker and others, 2002;Rutz and Bluff, 2008).…”

Section: Wildlife Cameras Camera Optionsmentioning

confidence: 99%

“…Researchers reported that individual camera systems can be installed in as little as 20 minutes (min) (McQuillen and Brewer, 2000) with an entire set of cameras for a study installed in 1 day (Lyra-Jorge and others, 2008). Lewis and others (2004) reported an average of 75 min to install miniature video cameras in nests that averaged 14.9 meters in height.…”

Section: Time To Assemblementioning

confidence: 99%

See 2 more Smart Citations

Use of wildlife webcams - Literature review and annotated bibliography

Ratz¹,

Conk²

2010

Open-File Report

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Section: System Maintenance and Monitoringmentioning

confidence: 99%

Section: Wildlife Cameras Camera Optionsmentioning

confidence: 99%

Section: Time To Assemblementioning

confidence: 99%

See 1 more Smart Citation

Use of wildlife webcams - Literature review and annotated bibliography

Ratz¹,

Conk²

2010

Open-File Report

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“…in which b i is a "reward baseline" for element θ i , computed as the inter-trial weighted mean of rewards, using the per-trial weight ( ∑ H k=0 ∇ θ i log Pr(u k |s k ; θ)) 2 , and ⟨⋅⟩ is the average over some number N of trajectories. µ ∑ ∇ is the mean over trials of the ∑ ∇ θ i terms.…”

Section: Baseline Gradient Estimationmentioning

confidence: 99%

“…Such ground-based sensors can gather data unavailable to those on aircraft or satellites; for example, continuous proximal surveillance of flora [1] or fauna [2], or measurements that require physical interaction such as watershed runoff [3]. The sensors may be far from cellular networks, have insufficient power or size to use long-range radio, and collect too much data to upload via satellite.…”

Section: Introductionmentioning

confidence: 99%

Model-Free Trajectory Optimisation for Unmanned Aircraft Serving as Data Ferries for Widespread Sensors

Pearre

Brown

2012

Remote Sensing

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Given multiple widespread stationary data sources such as ground-based sensors, an unmanned aircraft can fly over the sensors and gather the data via a wireless link. Performance criteria for such a network may incorporate costs such as trajectory length for the aircraft or the energy required by the sensors for radio transmission. Planning is hampered by the complex vehicle and communication dynamics and by uncertainty in the locations of sensors, so we develop a technique based on model-free learning. We present a stochastic optimisation method that allows the data-ferrying aircraft to optimise data collection trajectories through an unknown environment in situ, obviating the need for system identification. We compare two trajectory representations, one that learns near-optimal trajectories at low data requirements but that fails at high requirements, and one that gives up some performance in exchange for a data collection guarantee. With either encoding the ferry is able to learn significantly improved trajectories compared with alternative heuristics. To demonstrate the versatility of the model-free learning approach, we also learn a policy to minimise the radio transmission energy required by the sensor nodes, allowing prolonged network lifetime.

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An audio–visual nest monitoring system for the study and manipulation of siblicide in bearded vultures Gypaetus barbatus on the island of Crete (Greece)

Grivas

Xirouchakis

Christodoulou

et al. 2008

J Ethol

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We describe here an automated surveillance system that was installed in a bearded vulture (Gypaetus barbatus) nest in Crete during [2003][2004][2005][2006] with the aim of studying siblicide in this species. The system caused only minimal levels of disturbance to the birds and was capable of operating autonomously for more than 1 week. It comprised (1) a nest monitoring subsystem (camera, microphone, battery with a charge controller and a transmitter together with an antenna), which was supported by a solar panel, and (2) a recording subsystem (antenna receiver, video signal controller and a remote controlled PC through a GSM modem) that compressed the audiovideo signal and provided real time monitoring. Two-egg clutches with a 7-day laying and hatching interval were recorded while hatching asynchrony was also determined through an analysis of the bioacoustic data. Food delivery rate and number of feeds to the first chick increased after the hatching of its sibling although not significantly so. Of the aggressive interactions recorded, 98% were initiated by the dominant chick and resulted in an average of 246 ± 157 peckings per day. Supplementary feeding did not produce any significant correlation between feeding rate and sibling aggression. The second chick survived 5 days and died as a result of starvation accelerated by sibling aggression. Intervention measures aimed at increasing the survival chance of the second chick should be undertaken when the latter is 1-2 days old.

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Methodological Considerations for Monitoring Wild Bird Nests Using Video Technology

Cited by 28 publications

References 6 publications

Use of wildlife webcams - Literature review and annotated bibliography

Use of wildlife webcams - Literature review and annotated bibliography

Model-Free Trajectory Optimisation for Unmanned Aircraft Serving as Data Ferries for Widespread Sensors

An audio–visual nest monitoring system for the study and manipulation of siblicide in bearded vultures Gypaetus barbatus on the island of Crete (Greece)

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