Skip to content
Frontiers
Search

Mini Review

Frontiers in Ethology ·

Volume 5 - 2026 | https://doi.org/10.3389/fetho.2026.1894883

Opportunities for monitoring feeding behavior using RFID technology to support bird welfare in zoos

  • Joseph Krahn1
  • Julia Petrou2
  • Morag Dick2
  • Sarra Gourlie2
  • Molly McGuire1*corresponding author
  1. 1Welfare Science Department, Toronto Zoo, Toronto, ON, Canada
  2. 2Nutrition Science Department, Toronto Zoo, Toronto, ON, Canada
Article metrics0 viewsView details
PDFCiteMetrics

Abstract

Birds are one of the most speciose taxa housed in zoos, but there is little research about their welfare and management in aviaries. Managing birds in large, complex, multi-species spaces potentially provides welfare benefits, but monitoring animals in these spaces can be challenging. Radio-frequency identification (RFID) technology is widely used for monitoring the behavior of wild birds but is underutilized for monitoring birds in zoos. RFID is a valuable technology allowing for data stream-style automated detections of presence, date, time, and location data. This review discusses the ways that RFID data can be used to interpret feeding and social behavior to inform data-driven management when placed at the feeding area, providing information on duration of feeding, number of bouts, preference and social association like feeding together and agonistic displacements. It then discusses how this information can be practically applied in a zoo scenario for collecting general information about animals for which little is known, monitoring welfare concerns, and assessing the potential effects of husbandry changes. This review then expands on how RFID can be paired with other tools such as visual observation, weighing scales, and selection feeders. This review highlights the importance of validating RFID detections against visual observations to ensure accuracy and understand biases. Lastly, this review discusses the value of this RFID data for conservation, and opportunities to implement RFID in other locations beyond the feeder.

Keywords

1 Introduction

Birds are one of the most speciose taxa housed in zoos and vary widely in their behavioral abilities and needs (Rose et al., 2019). For zoos, keeping birds contributes to conservation, education, and public engagement objectives (e.g., see Hutchins et al., 2018). In pursuit of these objectives, modern zoos increasingly prioritize animal welfare—defined by the World Association of Zoos and Aquariums (2026) as “how the animal experiences its own world and life through its association with pleasant experiences specific for that species [ … ] or unpleasant experiences”—as a core component of ethical management (Ward et al., 2018). Despite the widespread presence of birds in zoos, the recognized intelligence of a variety of species (Mellor, 2019; Güntürkün et al., 2024) and growing interest in avian emotions (Papini et al., 2019) and consciousness (Mellor, 2019), bird-focused studies represent <10% of welfare research in zoos/aquaria in recent decades (Rose et al., 2019; Binding et al., 2020).

Animal welfare is conceptualized through a variety of frameworks, including the three spheres model (emphasizing the interactions of biological functioning, natural living, and affective states; Fraser et al., 1997) and the five domains model (focusing on behavior, nutrition, health, environment, and mental states; Mellor and Beausoleil, 2015). The investigation of animal welfare uses a variety of indicators, including behavior, to estimate an animal’s subjective experience and health, helping inform management decisions (Ward et al., 2018). Systematic investigations of behavior, ideally with low cost and labor requirements, would help address this gap in bird welfare research and support evidence-informed management (Melfi, 2009; Rose et al., 2024).

Radio-frequency identification (RFID) is commonly used for collecting behavioral information by detecting animals at specific locations (Gibbons and Andrews, 2004) and can be set up for continuous and automatic data recording. These raw data include presence, date, time, and location information that can also be used to estimate time budgets (e.g., presence duration and frequency), movement patterns, and social association (reviewed in Bonter and Bridge, 2011). RFID systems have been used widely for wild birds (Bonter and Bridge, 2011) but few published studies have used RFID for monitoring birds in zoos (reviewed in Hassinger and Nippert-Eng, 2025). RFID systems often target locations animals consistently visit (Bonter and Bridge, 2011), which makes feeding locations in zoos particularly valuable for collecting behavioral data due to high data density and the well-established links between feeding behavior and fitness-related outcomes, including survival and reproduction (Stephens et al., 2008; Milligan et al., 2017).

This review outlines how implementing RFID technology around feeding areas can be used to better understand bird behavior and promote evidence-driven, holistic bird management in zoos.

2 Behavior monitoring challenges in aviaries

Large, environmentally complex aviaries can promote a broad range of natural behaviors and support the co-habitation of many individuals and species (Bračko and King, 2014). For instance, diverse combinations of physical features (e.g., water, substrates, perching) and food presentations can encourage different foraging ecologies (e.g., ground, arboreal, aerial, aquatic; Rose et al., 2024) and varied spatial designs can allow different types of flight (e.g., vertical movement, aerial agility, extended periods airborne; Rose et al., 2024; Brereton et al., 2025). As such, zoos are moving toward having more of these environmentally complex, mixed species aviaries (Rose et al., 2024). These spaces, however, can result in a variety of management challenges (e.g., social conflict, varied nutritional needs; Woods et al., 2022; Figure 1). And accurate visual or physical assessment of factors that reflect animal welfare (e.g., species-typical behavior, abnormal or displaced behaviors, physical health-influenced locomotion) can become more difficult in larger, more complex spaces, especially when housing many similar looking individuals (Herlin et al., 2021).

Illustration of behavioral information collected at or near feeders using RFID feeding stations (e.g., black rectangles). The image includes an example of RFID detection data visualized through a computer-based output and highlights how such data can be analyzed and used to inform bird management. The image also includes examples of endogenous and exogenous factors that may influence individual experience and resource use in birds. This image was created by the authors using Canva Pro ([www.canva.com](http://www.canva.com)).
Figure 1. Illustration of behavioral information collected at or near feeders using RFID feeding stations (e.g., black rectangles). The image includes an example of RFID detection data visualized through a computer-based output and highlights how such data can be analyzed and used to inform bird management. The image also includes examples of endogenous and exogenous factors that may influence individual experience and resource use in birds. This image was created by the authors using Canva Pro (www.canva.com).

RFID systems offer opportunities for overcoming monitoring challenges in these large complex aviaries (see Bonter and Bridge, 2011). For instance, RFID does not rely on clear sight lines or visual characteristics to identify individuals, but on detections of a wearable or implantable transponder. The log file generated from detections of transponders includes a unique bird ID, the time and date the detection occurred, and the reader that detected it.

3 Designing a RFID monitoring system

Building a RFID system requires 4 main components: 1) A transponder (tag) attached to an animal, 2) a tag reader, 3) an electromagnetic field generator, and 4) a power source (Bonter and Bridge, 2011). Passive integrated transponders (PIT) tags are commonly used as they are small and do not require an internal power source (Bonter and Bridge, 2011). PIT tags do not store data directly but are detected when passing within a reader’s limited range. The long-term effects of attached/implanted PIT tags on health and survival are generally considered low (e.g., Farr et al., 2021). However, handling and implanting tags can cause behavioral responses that suggest stress or pain (e.g., Schlicht and Kempenaers, 2018), surgical implanting or abrasions from wearable tags can become infected or cause injury (e.g., Oswald et al., 2018), and other health concerns (e.g., sarcomas) may be possible (reviewed in Manville et al., 2024). More research into the longer-term behavioral impacts of attached/implanted devices (including RFID PIT tags) on birds would be beneficial, especially as these technologies become more commonly used (Vandenabeele et al., 2011).

Tag readers can be commercially purchased, although custom building can be more cost effective and modifiable (Bridge and Bonter, 2011; Hou et al., 2015). Detection range and the size and number of readers required will vary based on desired information and birds’ behaviors. For example, ruby-throated hummingbirds (Archilochus colubris) perch in a defined area to feed, requiring a small antenna and range with only one animal feeding at a time (Hou et al., 2015). In contrast, flock feeding species may require larger or multiple antennae programmed to frequently rescan to capture multiple individuals around the feeder (Bonter and Bridge, 2011). Stations can be powered with small batteries, direct power, or solar panels. Metals in the exhibit, soils, and water can interfere with detections and should be considered with station placement (Bonter and Bridge, 2011; Yu and Qu, 2020).

Validating RFID systems against visual observations is critical for ensuring accuracy and defining biologically appropriate event thresholds (e.g., Robinson et al., 2009; Whitham and Miller, 2016). Hughes et al. (2021) provide a discussion regarding the limitations and potential biases from using RFID in ornithological research, including low detection accuracy (e.g., caused by fast movement speeds or technical errors) and reader designs that modify bird behavior. Hughes et al. (2021) also note that few studies report validation procedures, but they highlight Iserbyt et al. (2018), which is a valuable case study with guidelines for quantifying bird behavior with RFID. Research using RFID systems should be interpreted cautiously, especially when validation procedures are unclear.

4 Inferring behavior from RFID detections

The raw data from RFID monitoring can be transformed into additional behavioral information (Figure 1). At the feeder, there are two main types of non-mutually exclusive behavioral data that can be inferred from RFID stations: feeding and social.

4.1 Feeding behavior

Feeding behavior encompasses the behavioral choices and actions involved in locating, selecting, manipulating, and consuming food, as well as their timing and location (Stephens and Krebs, 1986). Feeding behavior can be closely linked to health, reproduction, and welfare outcomes (Nielsen et al., 2016; Milligan et al., 2017). Aspects of feeding behavior can be inferred from RFID detections based on time and location data. For instance, repeated detections at the same feeder allow estimation of feeding bout duration, visit frequency, and total time spent feeding (e.g., chickens, Gallus gallus domesticus, van der Sluis et al., 2021; hummingbirds, Hou et al., 2015).

4.2 Social behavior

Social behavior constitutes individuals’ interactions and relationships with others, varying in purpose, structure, and duration (Blumstein et al., 2010). Social behavior can impact resource access (Farine et al., 2014), learning (Slagsvold and Wiebe, 2011), physiological stress (Silva et al., 2018), and resilience (Edgar et al., 2015). Aspects of social behavior can be inferred from RFID detections based on time and location data. Colocalization of individuals (e.g., detections at the same time and station) may provide insight into feeding-partner choices (Kings et al., 2023), personality characteristics like sociability (Réale et al., 2007), and overall social networks and group dynamics (Chan et al., 2025; Hou et al., 2015). Alternatively, detecting one animal entering the feeding area and another leaving has been used to collect agonistic interactions (Huzzey et al., 2014) and infer dominance hierarchies (Foris et al., 2019).

5 Practical applications

5.1 Establishing behavioral baselines

Animal behaviors are often repeatable (Bell et al., 2009). In birds, consistent patterns have been described for feeding behavior (in Sturnus vulgaris, Bateson and Nolan, 2022) and foraging site fidelity (in Rissa tridactyla, Harris et al., 2020). Continuous and longitudinal RFID monitoring at feeders can enable longitudinal characterization of feeding behavior (e.g., in Calypte anna and Selasphorus sasin; Bandivadekar et al., 2018), and can offer insight into behavioral patterns, choices, and constraints (Tetley and O’Hara, 2012; McMahon et al., 2024).

Understanding normal behavioral patterns can also reveal whether specific individuals are consistently disadvantaged (Philson et al., 2022). Social constraints may cause unequal distribution of nutrition or energy due to competition (Philson et al., 2022), which may have detrimental consequences for health and affective states (Dawkins, 2003). Receiving agonistic interactions or being socially subordinate can have negative implications for affective states (e.g., anhedonia; rats, Rygula et al., 2005), change behavior (e.g., decreased social exploration; Parus major, Carere et al., 2001), and influence stress-related biomarkers (e.g., elevated fecal glucocorticoid metabolites; chickens; Carvalho et al., 2018). Understanding who is disadvantaged allows for intervention to mitigate negative consequences. Within this framework, consistent expression of some feeding-related behaviors may support identification of positive affective states (e.g., exploration, affiliative behavior; Mellor, 2015). Stable social associations inferred from feeder co-occurrence with RFID may inform paired animal transfers or indicate mating pairs (Alba et al., 2019). Despotic or consistently antagonistic relationships may signal the need for group composition adjustments (e.g., in Leptoptilos crumeniferus; Valuska et al., 2014). Rapid successions of RFID detections at feeders can indicate atypically high competition (Philson et al., 2022; Sheng et al., 2024), which can be used to adjust feeding schedules, increase the number of feeding locations, or modify feeder type or placement to reduce crowding and pressure at high-use sites. Atypically low detection rates may indicate resource monopolization or elevated perceived risk (e.g., landscape of fear; Bonter et al., 2013; Beyer et al., 2010).

5.2 Monitoring behavioral change in a dynamic life

Monitoring deviations from established behavioral baselines can offer insight into an individual animal’s welfare (Dawkins, 2003). Knowledge of these deviations can facilitate welfare-focused management that considers the influence of exogenous and endogenous factors on an individual’s experience and modifies or sustains care processes to maximize behaviors that reflect positive welfare (e.g., competence building, affiliative behavior) and minimize distress (e.g., pain). Feeding and social behavior can be sensitive to changes in factors that influence welfare, which can be valuable to inform husbandry (e.g., medical intervention, social composition change). For example, Tillman and Adelman (2025) reported that house finches (Haemorhous mexicanus) modified their foraging decisions when experiencing infection, consistent with broader evidence that animals alter foraging behavior when experiencing health challenges, including injury and illness (reviewed in Rennolds and Bely, 2023). Similarly, chronic pain in mice was found to reverse dominance relationships (Tansley et al., 2019), house sparrows (Passer domesticus) adjusted their social position with changing physiological costs (Lindstrom et al., 2005), and jackdaws (Corvus monedula) altered their social relationships when the value of those associations changed (Kings et al., 2023). Consistency in behavior during potentially stressful events, or rapid return to baseline afterward, may indicate resilience to or limited influence of perceived stressors (Ardila‐Villamizar et al., 2025). Castano et al., 2025 found that Olrog’s gulls (Larus atlanticus) showed longer latencies to approach and consume novel food when presented alongside unfamiliar objects. This highlights how management decisions can affect individual experience and resource use, providing valuable information for future care procedures.

5.3 RFID monitoring roadmap

Ideally, RFID systems are readily implementable and have practical applications. Drawing inspiration from Bonter and Bridge (2011) and other cited resources (e.g., Hughes et al., 2021), Figure 2 provides a flowchart approach to aid animal care professionals and researchers in implementing RFID systems in aviaries. This approach begins by encouraging the investigator to review their current approach for collecting feeding and social behavior data. For those without an RFID system, it outlines required materials and useful references. The next step makes clear that validation against visual observations is essential for effective RFID systems. In the remaining sections, this figure highlights practical applications of these systems and additional tools that can be integrated with RFID (also outlined in more detail in the following section).

A flowchart for implementing feeder-based RFID monitoring in zoo-managed birds. The flowchart outlines when RFID systems may be appropriate for behavioral monitoring, key steps in system implementation and validation, and application for evidence-based management and welfare assessments. This framework builds on established RFID practices in avian research (e.g., Bonter and Bridge, 2011). This image was created by the authors using Canva Pro ([www.canva.com](http://www.canva.com)).
Figure 2. A flowchart for implementing feeder-based RFID monitoring in zoo-managed birds. The flowchart outlines when RFID systems may be appropriate for behavioral monitoring, key steps in system implementation and validation, and application for evidence-based management and welfare assessments. This framework builds on established RFID practices in avian research (e.g., Bonter and Bridge, 2011). This image was created by the authors using Canva Pro (www.canva.com).

6 Building integrative monitoring systems

To build integrative holistic management systems, RFID feeding stations can be paired with other tools or techniques.

6.1 Visual observations

Visual observations remain essential for interpreting specific behavioral events in detail. Events and individuals can be identified with RFID and efficiently targeted with live observations. Cameras have been successfully integrated with RFID systems, for example, to monitor plumage condition in penguins (Aptenodytes patagonicus; Gendner et al., 2005) and quantify diet composition and consumption rates in great tits (Parus major; Wilkin et al., 2009).

6.2 Weigh scales

Body mass is a foundational metric for monitoring health (Sibanda et al., 2020; Piseddu et al., 2024). Weighing birds can be challenging for a variety of reasons, including their ability to avoid humans in large aviaries and difficulty distinguishing individuals. RFID-equipped scales can be designed to accommodate a variety of diets and feeding styles such as nectar feeders (Hou et al., 2015), raised platform feeders (Bateson and Nolan, 2022), and ground feeders (Kerry et al., 1993; Černilová et al., 2023). Integrating an RFID-equipped scale provides opportunities to measure the amount and rate of feed consumption (e.g., Huzzey et al., 2006; Bateson and Nolan, 2022), as well as fluctuations in body mass (e.g., dark-eyed Juncos, Junco hyemalis; Vézina et al., 2001), offering insight into individual feeding and health trends.

6.3 Nutritional assessment

Combining RFID systems with diet reviews can deepen our understanding of nutritional landscapes in managed care, where feeding opportunities differ from natural foraging patterns. As certain species or individuals may consistently feed at particular locations, the diets offered there can be tailored to meet their specific nutritional requirements. Toddes et al. (2003) provided diets in locations “favored” by target birds and facilitate individual-specific feeding and intake monitoring. Péron and Grosset (2014) demonstrated that captive parrots over-select high-energy items when available, contrasting their natural foraging ecology and a pattern that can lead to imbalanced energy intake. Establishing baseline feeding and intake patterns enables evaluation of the effect of diet changes on individuals and provides valuable health information.

6.4 Selection feeders

Pairing RFID readers with “smart” food-dispensing systems can allow selective and individualized food delivery tied to individual tag ID (Small et al., 2013; Kings et al., 2023). In zoo settings, selection feeders may help manage nutritional inequality by tailoring diet delivery to individual needs, paralleling precision-feeding systems currently used in agriculture where automated feeders can adjust nutrient supply to individual animals (Andretta et al., 2016). Controlled, individual-specific feed and supplement distribution may be especially relevant in zoo aviaries where species with differing nutritional and medical needs are housed together.

6.5 Temperature sensors

Body temperature is a valuable physiological parameter in birds, providing insight into an animal’s thermoregulation, physiology, behavior, or responses to changes in the environment (Sunday et al., 2010; Khaliq et al., 2014). Jax et al. (2021) found that body temperature increased during an immune challenge in mallard ducks (Anas platyrhynchos), demonstrating its use as an indicator of compromised health. Recent studies have incorporated RFID PIT tags with temperature sensors to simultaneously record internal body temperature and collect behavioral data, such as foraging activity (e.g., chickadees, Poecile atricapillus; Hawkshaw et al., 2025). This integrated approach is promising for interpreting behavioral data in a physiological context and may provide an additional indicator of compromised health.

7 Future opportunities

Monitoring birds’ use of non-feeder resources can also provide useful information. Bailey et al. (2018) used RFID to determine the factors that influence forest gap-crossing decisions in chickadees. This type of study could be used in zoos to understand factors that influence movement between enclosures and prioritize preferred routes. RFID has been used to investigate environmental preference in chickens (Sales et al., 2015), an approach which could be used to inform substrate provisions for zoo-housed species. RFID systems can also help with assessment of learning, habituation, and engagement, for example, by recording interactions with RFID-equipped items (Chauhan et al., 2024). RFID-equipped feeders have been used to facilitate cognitive tests on wild birds (OpenFeeder, Cauchoix et al., 2022), which could be valuable in zoos for individual-specific cognitive development plans that align with principles of positive welfare (Rault et al., 2025).

Beyond routine management, RFID monitoring systems have applications in species breeding and reintroduction programs. Alba et al. (2019) used RFID to track real-time updates on breeding events and mating pairs; they also combined RFID with video recordings to identify birds that broke eggs, committed infanticide, or neglected their chicks. This information can be used to remove at-risk eggs and identify successful pairs for continued investment in conservation programs. Behavior is recognized as a key determinant of translocation success, post-release establishment, and survival (Goldenberg et al., 2019; Tetzlaff et al., 2019). For example, a study on whooping cranes (Grus americana) found that pre-release foraging and walking behaviors were significantly associated with post-release survival (Kreger et al., 2006). Within this context, RFID-derived behavioral baselines can help identify individuals exhibiting ecologically relevant behaviors, aiding in release decision-making. Similarly, providing animals in managed care with opportunities to develop and practice species-typical behaviors such as problem solving, foraging, and navigating complex environments has been shown to improve their preparedness for wild conditions (reviewed in Reading et al., 2013). For example, Cabot’s Tragopan pheasants (Tragopan caboti) with longer exposure to a more complex pre-release habitats had greater post-release survival (Liu et al., 2016). In these more complex environments, RFID can aid in tracking individuals, especially when maintaining visible data collection is challenging.

8 Summary

RFID monitoring systems provide an opportunity for continuous, individual-level data collection of bird behavior in zoos. The implementation of these systems benefits from transparent validation procedures and careful consideration of limitations and biases in data interpretation. RFID monitoring may help establish new knowledge of bird behavior and support evidence-driven decision-making in zoos.

Generative AI statement

The author(s) declared that generative AI was used in the creation of this manuscript. Generative AI (ChatGPT; GPT-5.3 OpenAI, San Francisco, CA,USA) was used for occasional language refinement in the editing of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

Edited by: Michelle O’Brien, Wildfowl & Wetlands Trust, United Kingdom

Reviewed by: Paul Rose, University of Exeter, United Kingdom

Robert Kelly, University of Exeter, United Kingdom

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Author contributions

JK: Writing – review & editing, Writing – original draft, Conceptualization. JP: Writing – review & editing, Writing – original draft, Conceptualization. MD: Resources, Conceptualization, Writing – review & editing, Supervision. SG: Supervision, Conceptualization, Writing – review & editing, Resources. MM: Supervision, Writing – review & editing, Resources, Conceptualization.

Acknowledgements

The authors thank the Toronto Zoo Wildlife Care Staff of the Australasia pavilion for contributions to bird monitoring discussions. We thank the Toronto Zoo’s Welfare and Wildlife Nutrition Science Staff for their support of this project and Dr. Gabriela Mastromonaco (Toronto Zoo) for reviewing this manuscript. We are thankful to the reviewers whose constructive comments helped improve this manuscript.

References

  1. Alba A. C., Breeding S., Valuska A. J., Sky C., Dunn M., Schutz P. J.. (2019). Use of passive radio frequency identification technologies to monitor nest usage in the northern carmine bee‐eater (Merops n. nubicus). Zoo Biol. 38, 498 – 507. doi: doi:10.1002/zoo.21514
  2. Andretta I., Pomar C., Kipper M., Hauschild L., Rivest J. (2016). Feeding behavior of growing–finishing pigs reared under precision feeding strategies. J. Anim. Sci. 94, 3042 – 3050. doi: doi:10.2527/jas.2016-0392
  3. Ardila‐Villamizar M., Sandoval D. T., Maldonado‐Chaparro A. A. (2025). Birds of a feather resist together: sociality and species predict the resilience and recovery strategies of two neotropical birds. Ecol. Evol. 15, e71668. doi: doi:10.1002/ece3.71668
  4. Bailey J. M., Reudink M. W., LaZerte S. E., Paetkau M., Johnson C. J., Hill D. J.. (2018). Using radio frequency identification (RFID) to investigate the gap-crossing decisions of Black-capped Chickadees (Poecile atricapillus). Auk: Ornithological Adv. 135, 449 – 460. doi: doi:10.1642/auk-17-162.1
  5. Bandivadekar R. R., Pandit P. S., Sollmann R., Thomas M. J., Logan S. M., Brown J. C.. (2018). Use of RFID technology to characterize feeder visitations and contact network of hummingbirds in urban habitats. PloS One 13, e0208057. doi: doi:10.1371/journal.pone.0208057
  6. Bateson M., Nolan R. (2022). A refined method for studying foraging behavior and body mass in group-housed European starlings. Animals 12, 1159. doi: doi:10.3390/ani12091159
  7. Bell A. M., Hankison S. J., Laskowski K. L. (2009). The repeatability of behaviour: a meta-analysis. Anim. Behav. 77, 771 – 783. doi: doi:10.1016/j.anbehav.2008.12.022
  8. Beyer H. L., Haydon D. T., Morales J. M., Frair J. L., Hebblewhite M., Mitchell M.. (2010). The interpretation of habitat preference metrics under use–availability designs. Philos. Trans. R. Soc. B. Biol. Sci. 365, 2245. doi: doi:10.1098/rstb.2010.0083
  9. Binding S., Farmer H., Krusin L., Cronin K. (2020). Status of animal welfare research in zoos and aquariums: Where are we, where to next ? J. Zoo Aquarium Res. 8, 166 – 174. doi: doi:10.19227/jzar.v8i3.505
  10. Blumstein D. T., Ebensperger L., Hayes L., Vásquez R. A., Ahern T. H., Burger J. R.. (2010). Towards an integrative understanding of social behavior: new models and new opportunities. Front. Behav. Neurosci. 4, 1850. doi: doi:10.3389/fnbeh.2010.00034
  11. Bonter D. N., Bridge E. S. (2011). Applications of radio frequency identification (RFID) in ornithological research: a review. J. Field Ornith. 82, 1 – 10. doi: doi:10.1111/j.1557-9263.2010.00302.x
  12. Bonter D. N., Zuckerberg B., Sedgwick C. W., Hochachka W. M. (2013). Daily foraging patterns in free-living birds: exploring the predation–starvation trade-off. Proc. R. Soc. B. Biol. Sci. 280, 20123087. doi: doi:10.1098/rspb.2012.3087
  13. Bračko A., King C. E. (2014). Advantages of aviaries and the Aviary Database Project: a new approach to an old housing option for birds. Int. Zoo Yearb. 48, 166 – 183. doi: doi:10.1111/izy.12035
  14. Brereton J. E., Coe J. C., Fernandez E. J. (2025). Future aviary design: The science of circular flight aviaries for avian welfare. Zoo Biol. 44, 293 – 303. doi: doi:10.1002/zoo.21903
  15. Bridge E. S., Bonter D. N. (2011). A low‐cost radio frequency identification device for ornithological research. J. Field Ornith. 82, 52 – 59. doi: doi:10.1111/j.1557-9263.2010.00307.x
  16. Carere C., Welink D., Drent P. J., Koolhaas J. M., Groothuis T. G. (2001). Effect of social defeat in a territorial bird (Parus major) selected for different coping styles. Physiol. Behav. 73, 427 – 433. doi: doi:10.1016/s0031-9384(01)00492-9
  17. Carvalho R. R., Palme R., da Silva Vasconcellos A. (2018). An integrated analysis of social stress in laying hens: The interaction between physiology, behaviour, and hierarchy. Behav. Processes 149, 43 – 51. doi: doi:10.1016/j.beproc.2018.01.016
  18. Castano M. V., Biondi L. M., Biagiotti Barchiesi M. C., García G. O. (2025). Object neophobia in the Olrog's gull and its relationship with propensity to try novel food. J. Zool. 327, 410 – 419. doi: doi:10.1111/jzo.70067
  19. Cauchoix M., Barragan Jason G., Biganzoli A., Briot J., Guiraud V., El Ksabi N.. (2022). The OpenFeeder: a flexible automated RFID feeder to measure interspecies and intraspecies differences in cognitive and behavioural performance in wild birds. Methods Ecol. Evol. 13, 1955 – 1961. doi: doi:10.1111/2041-210x.13931
  20. Černilová B., Linda M., Kuře J., Hromasová M., Chotěborský R., Krunt O. (2023). Validation of an IoT System Using UHF RFID Technology for Goose Growth Monitoring. Agriculture 14 (1), 76.
  21. Chan A. H. H., Dunning J., Beck K. B., Burke T., Chik H. Y. J., Dunleavy D.. (2025). Animal social networks are robust to changing association definitions. Behav. Ecol. Sociobiol. 79, 26. doi: doi:10.32942/x2p890
  22. Chauhan M. S., Chauhan A., Bayen M., Wu F., Althukair F. A., Kaiser M. T.. (2024). eNut: a sensing system to measure the acquisition of foraging proficiency in wild tree squirrels. IEEE Sens. J. 24, 30930 – 30946. doi: doi:10.1109/jsen.2024.3435834
  23. Dawkins M. S. (2003). Behaviour as a tool in the assessment of animal welfare. Zoology 106, 383 – 387. doi: doi:10.1078/0944-2006-00122
  24. Edgar J., Held S., Paul E., Pettersson I., Price R. I. A., Nicol C. (2015). Social buffering in a bird. Anim. Behav. 105, 11 – 19. doi: doi:10.1016/j.anbehav.2015.04.007
  25. Farine D. R., Aplin L. M., Garroway C. J., Mann R. P., Sheldon B. C. (2014). Collective decision making and social interaction rules in mixed-species flocks of songbirds. Anim. Behav. 95, 173 – 182. doi: doi:10.1016/j.anbehav.2014.07.008
  26. Farr J. J., Haave‐Audet E., Thompson P. R., Mathot K. J. (2021). No effect of passive integrated transponder tagging method on survival or body condition in a northern population of black‐capped chickadees (Poecile atricapillus). Ecol. Evol. 11, 9610 – 9620. doi: doi:10.1002/ece3.7783
  27. Foris B., Zebunke M., Langbein J., Melzer N. (2019). Comprehensive analysis of affiliative and agonistic social networks in lactating dairy cattle groups. Appl. Anim. Behav. Sci. 210, 60 – 67. doi: doi:10.1016/j.applanim.2018.10.016
  28. Fraser D., Weary D. M., Pajor E. A., Milligan B. N. (1997). A scientific conception of animal welfare that reflects ethical concerns. Anim. Welfare 6, 187 – 205. doi: doi:10.1017/s0962728600019795
  29. Gendner J. P., Gauthier-Clerc M., Le Bohec C., Descamps S., Le Maho Y. (2005). A new application for transponders in studying penguins. J. Field Ornith. 76, 138 – 142. doi: doi:10.1648/0273-8570-76.2.138
  30. Gibbons W. J., Andrews K. M. (2004). PIT tagging: simple technology at its best. Bioscience 54, 447 – 454. doi: doi:10.1641/0006-3568(2004)054[0447:ptstai]2.0.co;2
  31. Goldenberg S. Z., Owen M. A., Brown J. L., Wittemyer G., Oo Z. M., Leimgruber P. (2019). Increasing conservation translocation success by building social functionality in released populations. Global Ecol. Conserv. 18, e00604. doi: doi:10.1016/j.gecco.2019.e00604
  32. Güntürkün O., Pusch R., Rose J. (2024). Why birds are smart. Trends Cognit. Sci. 28, 197 – 209. doi: doi:10.1016/j.tics.2023.11.002
  33. Harris S. M., Descamps S., Sneddon L. U., Bertrand P., Chastel O., Patrick S. C. (2020). Personality predicts foraging site fidelity and trip repeatability in a marine predator. J. Anim. Ecol. 89, 68 – 79. doi: doi:10.1111/1365-2656.13106
  34. Hassinger L., Nippert-Eng C. (2025). Beyond the camera trap: A systematic review of computing technology used to monitor and interact with (more) varied taxa in zoos and aquariums. Animals 15, 1721. doi: doi:10.3390/ani15121721
  35. Hawkshaw D. M., Wijmenga J. J., Mathot K. J. (2025). Individual variation in diurnal body temperature and foraging activity in overwintering black-capped chickadees (Poecile atricapillus). J. Therm. Biol. 127, 104059. doi: doi:10.2139/ssrn.4819002
  36. Herlin A., Brunberg E., Hultgren J., Högberg N., Rydberg A., Skarin A. (2021). Animal welfare implications of digital tools for monitoring and management of cattle and sheep on pasture. Animals 11, 829. doi: doi:10.3390/ani11030829
  37. Hou L., Verdirame M., Welch K. C. (2015). Automated tracking of wild hummingbird mass and energetics over multiple time scales using radio frequency identification (RFID) technology. J. Avian Biol. 46, 1 – 8. doi: doi:10.1111/jav.00478
  38. Hughes E. J., Mady R. P., Bonter D. N. (2021). Evaluating the accuracy and biological meaning of visits to RFID‐enabled bird feeders using video. Ecol. Evol. 11, 17132 – 17141. doi: doi:10.1002/ece3.8352
  39. Hutchins M., Marra P. P., Diebold E., Kreger M. D., Sheppard C., Hallager S.. (2018). The evolving role of zoological parks and aquariums in migratory bird conservation. Zoo Biol. 37, 360 – 368. doi: doi:10.1002/zoo.21438
  40. Huzzey J. M., DeVries T. J., Valois P., von Keyserlingk M. A. G. (2006). Stocking density and feed barrier design affect the feeding and social behavior of dairy cattle. J. Dairy Sci. 89, 126 – 133. doi: doi:10.3168/jds.s0022-0302(06)72075-6
  41. Huzzey J. M., Weary D. M., Tiau B. Y. F., Von Keyserlingk M. A. G. (2014). Automatic detection of social competition using an electronic feeding system. J. Dairy Sci. 97 (5), 2953 – 2958.
  42. Iserbyt A., Griffioen M., Borremans B., Eens M., Müller W. (2018). How to quantify animal activity from radio‐frequency identification (RFID) recordings. Ecol. Evol. 8 (20), 10166 – 10174.
  43. Jax E., Müller I., Börno S., Borlinghaus H., Eriksson G., Fricke E.. (2021). Health monitoring in birds using bio-loggers and whole blood transcriptomics. Sci. Rep. 11, 10815. doi: doi:10.1038/s41598-021-90212-8
  44. Kerry K., Clarke J., Else G. (1993). “ The use of an automated weighing and recording system for the study of the biology of adelie penguins (Pygoscelis adeliae),” in (14th Symposium on Polar Biology). Proceedings of the NIPR Symposium on Polar Biology, vol. 6. (Tokyo: National Institute of Polar Research), 62 – 75.
  45. Khaliq I., Hof C., Prinzinger R., Böhning-Gaese K., Pfenninger M. (2014). Global variation in thermal tolerances and vulnerability of endotherms to climate change. Proc. R. Soc. B. Biol. Sci. 281, 20141097. doi: doi:10.1098/rspb.2014.1097
  46. Kings M., Arbon J. J., McIvor G. E., Whitaker M., Radford A. N., Lerner J.. (2023). Wild jackdaws can selectively adjust their social associations while preserving valuable long-term relationships. Nat. Commun. 14, 5103. doi: doi:10.1038/s41467-023-40808-7
  47. Kreger M. D., Hatfield J. S., Estevez I., Gee G. F., Clugston D. A. (2006). Behavioral profiles of the captive juvenile whooping crane as an indicator of post‐release survival. Zoo Biology: Published affiliation Am. Zoo Aquarium Assoc. 25, 11 – 24. doi: doi:10.1002/zoo.20075
  48. Lindström K. M., Hasselquist D., Wikelski M. (2005). House sparrows (Passer domesticus) adjust their social status position to their physiological costs. Horm. Behav. 48 (3), 311 – 320.
  49. Liu B., Li L., Lloyd H., Xia C., Zhang Y., Zheng G. (2016). Comparing post-release survival and habitat use by captive-bred Cabot’s Tragopan (Tragopan caboti) in an experimental test of soft-release reintroduction strategies. Avian Res. 7, 19. doi: doi:10.1186/s40657-016-0053-2
  50. Manville A. M., Levitt B. B., Lai H. C. (2024). Health and environmental effects to wildlife from radio telemetry and tracking devices—state of the science and best management practices. Front. Vet. Sci. 11, 1283709. doi: doi:10.3389/fvets.2024.1283709
  51. McMahon K., Marples N. M., Spurgin L. G., Rowland H. M., Sheldon B. S., Firth J. A. (2024). Social network centrality shapes dietary decisions in a wild bird population. iScience 27, 109581. doi: doi:10.2139/ssrn.4552285
  52. Melfi V. A. (2009). There are big gaps in our knowledge, and thus approach, to zoo animal welfare: a case for evidence‐based zoo animal management. Zoo Biol. 28, 574 – 588. doi: doi:10.1002/zoo.20288
  53. Mellor D. J. (2015). Positive animal welfare states and encouraging environment-focused and animal-to-animal interactive behaviours. N. Z. Vet. J. 63, 9 – 16. doi: doi:10.1080/00480169.2014.926800
  54. Mellor D. J. (2019). Welfare-aligned sentience: Enhanced capacities to experience, interact, anticipate, choose and survive. Animals 9, 440. doi: doi:10.3390/ani9070440
  55. Mellor D. J., Beausoleil N. J. (2015). Extending the ‘five domains’ model for animal welfare assessment to incorporate positive welfare states. Anim. Welfare 24, 241 – 253. doi: doi:10.7120/09627286.24.3.241
  56. Milligan N. D., Radersma R., Cole E. F., Sheldon B. C. (2017). To graze or gorge: Consistency and flexibility of individual foraging tactics in tits. J. Anim. Ecol. 86, 826 – 836. doi: doi:10.1111/1365-2656.12651
  57. Nielsen B. L., de Jong I. C., De Vries T. J. (2016). “ The use of feeding behaviour in the assessment of animal welfare,” in Nutrition and the Welfare of Farm Animals, ed. Phillips C. J. C., (Cham: Springer International Publishing), 59 – 84.
  58. Oswald K. N., Evlambiou A. A., Ribeiro Â.M., Smit B. (2018). Tag location and risk assessment for passive integrated transponder‐tagging passerines. Ibis 160, 453 – 457. doi: doi:10.1111/ibi.12558
  59. Papini M. R., Penagos-Corzo J. C., Pérez-Acosta A. M. (2019). Avian emotions: Comparative perspectives on fear and frustration. Front. Psychol. 9, 2707. doi: doi:10.3389/fpsyg.2018.02707
  60. Péron F., Grosset C. (2014). The diet of adult psittacids: Veterinarian and ethological approaches. J. Anim. Physiol. Anim. Nutr. 98, 403 – 416. doi: doi:10.1111/jpn.12103
  61. Philson C. S., Pelletier T. A., Foltz S. L., Davis J. E. (2022). Using machine learning to identify associations between the environment, occurrence, and outcomes of songbird displacements at supplemental feeders. Birds 3, 306 – 319. doi: doi:10.3390/birds3030021
  62. Piseddu A., van Zeeland Y. R., Rault J. L. (2024). What we (don’t) know about parrot welfare: Finding welfare indicators through a systematic literature review. Anim. Welfare 33, e57. doi: doi:10.1017/awf.2024.61
  63. Rault J. L., Bateson M., Boissy A., Forkman B., Grinde B., Gygax L.. (2025). A consensus on the definition of positive animal welfare. Biol. Lett. 21, 20240382. doi: doi:10.1098/rsbl.2024.0382
  64. Reading R. P., Miller B. J., Shepherdson D. (2013). The value of enrichment to reintroduction success. Zoo Biol. 32, 332 – 341. doi: doi:10.1002/zoo.21054
  65. Réale D., Reader S. M., Sol D., McDougall P. T., Dingemanse N. J. (2007). Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291 – 318. doi: doi:10.1111/j.1469-185X.2007.00010.x
  66. Rennolds C. W., Bely A. E. (2023). Integrative biology of injury in animals. Biol. Rev. 98, 34 – 62. doi: doi:10.1111/brv.12894
  67. Robinson E. J., Richardson T. O., Sendova-Franks A. B., Feinerman O., Franks N. R. (2009). Radio tagging reveals the roles of corpulence, experience and social information in ant decision making. Behav. Ecol. Sociobiol. 63, 627 – 636. doi: doi:10.1007/s00265-008-0696-z
  68. Rose P. E., Brereton J. E., Rowden L. J., de Figueiredo R. L., Riley L. M. (2019). What’s new from the zoo? An analysis of ten years of zoo-themed research output. Palgrave Commun. 5, 1 – 10. doi: doi:10.1057/s41599-019-0345-3
  69. Rose P., Freeman M., Hickey I., Kelly R., Greenwell P. (2024). Considering what animals “need to do” in enclosure design: Questions on bird flight and aviaries. Birds 5, 586 – 603. doi: doi:10.3390/birds5030039
  70. Rygula R., Abumaria N., Flügge G., Fuchs E., Rüther E., Havemann-Reinecke U. (2005). Anhedonia and motivational deficits in rats: Impact of chronic social stress. Behav. Brain Res. 162, 127 – 134. doi: doi:10.1016/j.bbr.2005.03.009
  71. Sales G. T., Green A. R., Gates R. S., Brown-Brandl T. M., Eigenberg R. A. (2015). Quantifying detection performance of a passive low-frequency RFID system in an environmental preference chamber for laying hens. Comput. Electron. Agric. 114, 261 – 268. doi: doi:10.1016/j.compag.2015.03.008
  72. Schlicht E., Kempenaers B. (2018). The immediate impact of ringing, blood sampling and PIT-tag implanting on the behaviour of blue tits Cyanistes caeruleus. Ardea 106, 39 – 98. doi: doi:10.5253/arde.v106i1.a8
  73. Sheng K., Foris B., Krahn J., Weary D. M., von Keyserlingk M. A. (2024). Redefining dominance calculation: Increased competition flattens the dominance hierarchy in dairy cows. J. Dairy Sci. 107, 7286 – 7298. doi: doi:10.3168/jds.2023-24587
  74. Sibanda T. Z., Kolakshyapati M., Walkden-Brown S. W., de Souza Vilela J., Courtice J. M., Ruhnke I. (2020). Body weight sub-populations are associated with significant different welfare, health and egg production status in Australian commercial free-range laying hens in an aviary system. Eur. Poult. Sci. 84, 1 – 18. doi: doi:10.1399/eps.2020.295
  75. Silva L. R., Lardy S., Ferreira A. C., Rey B., Doutrelant C., Covas R. (2018). Females pay the oxidative cost of dominance in a highly social bird. Anim. Behav. 144, 135 – 146. doi: doi:10.1016/j.anbehav.2018.08.006
  76. Slagsvold T., Wiebe K. L. (2011). Social learning in birds and its role in shaping a foraging niche. Philos. Trans. R. Soc. B. Biol. Sci. 366, 969 – 977. doi: doi:10.1098/rstb.2010.0343
  77. Small T. W., Bridge E. S., Schoech S. J. (2013). Food supplementation of Florida scrub-jay (Aphelocoma coerulescens) nestlings: Long-term effects on hypothalamic-pituitary-adrenal axis responsiveness. Integr. Comp. Biol. 53, E201 – E201.
  78. Stephens D. W., Brown J. S., Ydenberg R. C. (2008). Foraging: Behavior and Ecology. (Chicago, IL: University of Chicago Press).
  79. Stephens D. W., Krebs J. R. (1986). Foraging Theory (Princeton, NJ: Princeton University Press).
  80. Sunday J. M., Bates A. E., Dulvy N. K. (2010). Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. B. Biol. Sci. 278, 1823. doi: doi:10.1098/rspb.2010.1295
  81. Tansley S. N., Tuttle A. H., Wu N., Tohyama S., Dossett K., Gerstein L.. (2019). Modulation of social behavior and dominance status by chronic pain in mice. Genes Brain Behav. 18, e12514. doi: doi:10.1111/gbb.12514
  82. Tetley C. L., O’Hara S. J. (2012). Ratings of animal personality as a tool for improving the breeding, management and welfare of zoo mammals. Anim. Welfare 21, 463 – 476. doi: doi:10.7120/09627286.21.4.463
  83. Tetzlaff S. J., Sperry J. H., DeGregorio B. A. (2019). Effects of antipredator training, environmental enrichment, and soft release on wildlife translocations: A review and meta-analysis. Biol. Conserv. 236, 324 – 331. doi: doi:10.1016/j.biocon.2019.05.054
  84. Tillman Jr. F. E., Adelman J. S. (2025). Infection alters foraging decisions in a common songbird. Animal Behaviour 219, 123023.
  85. Toddes B., Luu P., Baltz A. (2003). “ Training, enrichment and sound animal nutrition can coexist: The development of a multifaceted bird feeding program at the Philadelphia Zoo,” in Proceedings of the Fifth Conference on Zoo and Wildlife Nutrition, eds. Ward A., Brooks M., Maslanka M., (Minneapolis, MN: AZA Nutrition Advisory Group), 5 – 8.
  86. Valuska A. J., Leighty K. A., Ferrie G. M., Grand A. P., Ross J., Schutz P.. (2014). The effect of changes in group composition on social dynamics in marabou storks (Leptoptilos crumeniferus). Zoo Biol. 33, 150 – 154. doi: doi:10.1002/zoo.21110
  87. Vandenabeele S. P., Wilson R. P., Grogan A. (2011). Tags on seabirds: How seriously are instrument-induced behaviours considered ? Anim. Welfare 20, 559 – 571. doi: doi:10.1017/s0962728600003195
  88. van der Sluis M., Ellen E. D., de Klerk B., Rodenburg T. B., de Haas Y. (2021). The relationship between gait and automated recordings of individual broiler activity levels. Poult. Sci. 100, 101300. doi: doi:10.1016/j.psj.2021.101300
  89. Vézina F., Charlebois D., Thomas D. W. (2001). An automated system for the measurement of mass and identification of birds at perches. J. Field Ornith. 72, 211 – 220. doi: doi:10.1648/0273-8570-72.2.211
  90. Ward S. J., Sherwen S., Clark F. E. (2018). Advances in applied zoo animal welfare science. J. Appl. Anim. Welfare Sci. 21, 23 – 33. doi: doi:10.1080/10888705.2018.1513842
  91. Whitham J. C., Miller L. J. (2016). Using technology to monitor and improve zoo animal welfare. Anim. Welfare 25, 395 – 409. doi: doi:10.7120/09627286.25.4.395
  92. Wilkin T. A., King L. E., Sheldon B. C. (2009). Habitat quality, nestling diet, and provisioning behaviour in great tits Parus major. J. Avian Biol. 40, 135 – 145. doi: doi:10.1111/j.1600-048x.2009.04362.x
  93. Woods J. M., Eyer A., Miller L. J. (2022). Bird welfare in zoos and aquariums: General insights across industries. J. Zoological Botanical Gardens 3, 198 – 222. doi: doi:10.3390/jzbg3020017
  94. World Association of Zoos and Aquariums (2026). WAZA’s Approach to Animal Welfare. Our Approach to Animal Welfare - WAZA. Available online at: https://www.waza.org/our-approach-to-animal-welfare/ (Accessed June 23, 2026).
  95. Yu Y., Qu Y. (2020). “ Research on environmental factors affecting RFID reading performance ”, in: 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), (Piscataway, NJ: IEEE), 758 – 761. doi: doi:10.1109/ITOEC49072.2020.9141889

Citation

Krahn, J., et al. (2026). Opportunities for monitoring feeding behavior using RFID technology to support bird welfare in zoos. Frontiers in Ethology, Volume 5. https://doi.org/
Received
Accepted
Published
Volume
5 - 2026

Copyright

Copyright © 2026 Krahn, Petrou, Dick, Gourlie and McGuire.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Molly McGuire

Article metrics

Views
0
Downloads
0
Citations
0
Shares
0

Related articles

Original ResearchRepeatability in venom expenditure during defensive bites in rattlesnakes: use of a chemical weapon deployed to generate an extended phenotypeChelsea E. Martin, William K. HayesCorrectionCorrection: Effects of husbandry practices on Komodo dragon (Varanus komodoensis) behavior and welfare: a multi-institutional evaluationFaith Polando, Jennifer Campbell, Logan Opperman, Dustin Smith, Emily C. LynchOriginal ResearchLet’s get it together: the relationship between different measures of empathy in dogsKalyn A. Otzelberger, Teresa Romero, Julia E. Meyers-Manor