A note on fear reaction of three different genetic strains of laying hens to a simulated hawk attack in the hen run of a free-range system

Zeltner, Esther; Hirt, Helen

doi:10.1016/j.applanim.2007.12.003

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…A similar method could thus be applied to caged laying hens or small groups of hens to observe individual-level responses (Brendler, Kipper & Schrader, 2014) but larger-groups of hens would likely prevent accurate individual-level observations. A visual predator stimulus could also be used (Schütz, Forkman & Jensen, 2001) for which presentation to individual birds could be better controlled than auditory playbacks but could also be applied at a group level (Zeltner & Hirt, 2008). Caution would have to be placed on ensuring the stimulus was threatening to the hens.…”

Section: Discussionmentioning

confidence: 99%

Application of open field, tonic immobility, and attention bias tests to hens with different ranging patterns

Campbell

Dickson

2019

PeerJ

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Assessment of negative affective states is a key component of animal welfare research. In laying hens, excessive fearfulness results in reduced production and increased sensitivity to stress. Fearfulness can be defined as a response to a known threat, but anxiety is a response to an unknown threat and may have similar negative consequences. The open field test and tonic immobility test are commonly applied to measure fearfulness in laying hens. An attention bias test that measured individual hen’s responses to playback of a conspecific alarm call in the presence of food was recently pharmacologically validated using an anxiogenic drug but was confounded by the hen’s typical motionless response in a novel environment. The current study used 56-week old free-range layers to further assess the validity of an attention bias test to differentiate ranging treatment groups in comparison with the open field and tonic immobility tests. The selected hens varied in their range use patterns as tracked by radio-frequency identification technology. ‘Indoor’ hens did not access the range and ‘outdoor’ hens ranged daily; previous research has confirmed higher fearfulness in hens that remain indoors. The tonic immobility test did not differentiate ranging groups (P = 0.34), but indoor birds were slower to first step (P = 0.03) and stepped less (P = 0.02) in the open field test. The attention bias test occurred in an isolated wooden box using a conspecific alarm call playback (a threat) and mixed grain (a positive stimulus). The behavioural response of latency to resume eating following playback of the alarm call was measured to differentiate the anxiety states of the indoor and outdoor ranging birds. Before the attention bias test could occur, birds had to be habituated to the test box across three separate 5-minute sessions to increase the willingness to feed within the novel test environment. All birds ate faster across time (P < 0.001) but the indoor birds were slower to eat than the outdoor birds (P < 0.001). In this study, the latency to resume eating following an alarm call was determined to be a poor measure for highly anxious birds as they failed to eat at all. Forty-six percent of indoor hens were excluded for not eating across the 5-minute test. Of the birds that did eat, only 7% of indoor hens ate following playback of the alarm call, compared with 36% of outdoor hens. This repetition of an attention bias test for laying hens highlights the challenges in assessing hens with extreme fearful/anxious responses and that information may be missed when non-performing hens are excluded from behavioural tests. We suggest that latency to eat in a novel arena without any alarm call playback is an informative measure of anxious state that can be applied to all hens but consideration must be made of potential differences in food motivation.

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Section: Discussionmentioning

confidence: 99%

Application of open field, tonic immobility, and attention bias tests to hens with different ranging patterns

Campbell

Dickson

2019

PeerJ

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“…A similar method could thus be applied to caged laying hens or small groups of hens to observe individual-level responses (Brendler et al, 2014) but larger-groups of hens would likely prevent accurate individual-level observations. A visual predator stimulus could also be used (Schütz et al, 2001) for which presentation to individual birds could be better controlled than auditory playbacks but could also be applied at a group level (Zeltner and Hirt, 2008). Caution would have to be placed on ensuring the stimulus was threatening to the hens.…”

Section: Discussionmentioning

confidence: 99%

Peer Review #1 of "Application of open field, tonic immobility, and attention bias tests to hens with different ranging patterns (v0.1)"

Gebhardt-Henrich¹

2019

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Assessment of negative affective states is a key component of animal welfare research. In laying hens, excessive fearfulness results in reduced production and increased sensitivity to stress. Fearfulness can be defined as a response to a known threat, but anxiety is a response to an unknown threat and may have similar negative consequences. The open field test and tonic immobility test are commonly applied to measure fearfulness in laying hens. An attention bias test that measured individual hen's responses to playback of a conspecific alarm call in the presence of food was recently pharmacologically validated using an anxiogenic drug but was confounded by the hen's typical motionless response in a novel environment. The current study used 56-week old free-range layers to further assess the validity of an attention bias test to differentiate ranging treatment groups in comparison with the open field and tonic immobility tests. The selected hens varied in their range use patterns as tracked by radio-frequency identification technology. 'Indoor' hens did not access the range and 'outdoor' hens ranged daily; previous research has confirmed higher fearfulness in hens that remain indoors. The tonic immobility test did not differentiate ranging groups (P = 0.34), but indoor birds were slower to first step and stepped less (P = 0.02) in the open field test (P = 0.03). The attention bias test occurred in an isolated wooden box using a conspecific alarm call playback (a threat) and mixed grain (a positive stimulus). The behavioural response of latency to resume eating following playback of the alarm call was measured to differentiate the anxiety states of the indoor and outdoor ranging birds. Before the attention bias test could occur, birds had to be habituated to the test box across three separate 5 minute sessions to increase the willingness to feed within the novel test environment. All birds ate faster across time (P < 0.001) but the indoor birds were slower to eat than the outdoor birds (P < 0.001). In this study, the latency to resume eating following an alarm call was determined to be a poor measure for highly anxious birds as they failed to eat at all. Forty-six percent of indoor hens were excluded for not eating across the 5-minute test. Of the birds that did eat, only

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“…For example, Marx et al [ 5 ] studied the voices of chicks in the process of grouping, including distress calls, short peeps, warblers, and pleasure notes, and found that the voices would change with the growth environment and social ability. Zeltner et al [ 6 ] studied the fear reaction (especially the calls) of laying hens composed of the three different strains when they are attacked by a predator. Fontana et al [ 7 ] conducted a study on the peak frequency of broiler sound signals and concluded that there is a significant correlation between the peak frequency of broiler sound signals and its own weight.…”

Section: Introductionmentioning

confidence: 99%

Recognition Method for Broiler Sound Signals Based on Multi-Domain Sound Features and Classification Model

Tao

Wang

Sun

et al. 2022

Sensors

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In view of the limited number of extracted sound features, the lack of in-depth analysis of applicable sound features, and the lack of in-depth study of the selection basis and optimization process of classification models in the existing broiler sound classification or recognition research, the author proposes a recognition method for broiler sound signals based on multi-domain sound features and classification models. The implementation process is divided into the training stage and the testing stage. In the training stage, the experimental area is built, and multiple segments of broiler sound signals are collected and filtered. Through sub-frame processing and endpoint detection, the combinations of start frames and end frames of multiple sound types in broiler sound signals are obtained. A total of sixty sound features from four aspects of time domain, frequency domain, Mel-Frequency Cepstral Coefficients (MFCC), and sparse representation are extracted from each frame signal to form multiple feature vectors. These feature vectors are labeled manually to build the data set. The min-max standardization method is used to process the data set, and the random forest is used to calculate the importance of sound features. Then, thirty sound features that contribute more to the classification effect of the classification model are retained. On this basis, the classification models based on seven classification algorithms are trained, the best-performing classification model based on k-Nearest Neighbor (kNN) is obtained, and its inherent parameters are optimized. Then, the optimal classification model is obtained. The test results show that the average classification accuracy achieved by the decision-tree-based classifier (abbreviated as DT classifier) on the data set before and after min–max standardization processing is improved by 0.6%, the average classification accuracy achieved by the DT classifier on the data set before and after feature selection is improved by 3.1%, the average classification accuracy achieved by the kNN-based classification model before and after parameter optimization is improved by 1.2%, and the highest classification accuracy is 94.16%. In the testing stage, for a segment of the broiler sound signal collected in the broiler captivity area, the combinations of the start frames and end frames of multiple sound types in the broiler sound signal are obtained through signal filtering, sub-frame processing, endpoint detection, and other steps. Thirty sound features are extracted from each frame signal to form the data set to be predicted. The optimal classification model is used to predict the labels of each piece of data in the data set to be predicted. By performing majority voting processing on the predicted labels of the data combination corresponding to each sound type, the common labels are obtained; that is, the predicted types are obtained. On this basis, the definition of recognition accuracy for broiler sound signals is proposed. The test results show that the classification accuracy achieved by the optimal classification model on the data set to be predicted is 93.57%, and the recognition accuracy achieved on the multiple segments of the broiler sound signals is 99.12%.

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A note on fear reaction of three different genetic strains of laying hens to a simulated hawk attack in the hen run of a free-range system

Cited by 7 publications

References 11 publications

Application of open field, tonic immobility, and attention bias tests to hens with different ranging patterns

Application of open field, tonic immobility, and attention bias tests to hens with different ranging patterns

Peer Review #1 of "Application of open field, tonic immobility, and attention bias tests to hens with different ranging patterns (v0.1)"

Recognition Method for Broiler Sound Signals Based on Multi-Domain Sound Features and Classification Model

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