Context: Carnaby’s (Calyptorhychus latirostris), Baudin’s (Calyptorhynchus baudinii) and forest red-tailed black cockatoos (Calyptorhynchus banksii naso) are threatened parrot species endemic to south-western Australia. Behavioural monitoring has previously involved direct observation, which has proven challenging because of their cryptic nature, the type of habitat they move through and their speed of movement. The development of a model to accurately classify behaviour from tri-axial accelerometer data will provide greater insight into black cockatoo behaviour and ecology.

Aims: To develop an automated classifier model to classify accelerometer data from released black cockatoos to determine behaviour and activity budgets for three species of black cockatoo.

Methods: In the present study, we attached tri-axial accelerometers, housed in GPS tags, to four Carnaby’s cockatoos, three forest red-tailed black cockatoos and two Baudin’s cockatoos in captive care, undergoing rehabilitation for release back to the wild. Accelerometer data from these birds was coupled with 19 video files of the birds’ behaviour when flying, feeding and resting, to develop an automated behaviour classifier. The classifier was then used to annotate accelerometer data from 15 birds released after successful rehabilitation and to calculate activity budgets for these birds post-release.

Key results: We developed a classifier able to identify resting, flying and foraging behaviours from accelerometer data with 86% accuracy, as determined by the percentage of observed behaviours correctly identified by the classifier. The application of the classifier to accelerometer data from 15 released cockatoos enabled us to determine behaviours and activity budgets for all three species of black cockatoo. Black cockatoos spent most of their time at rest, followed by foraging with a short period of time flying.

Conclusions: Application of the classifier to data from released birds gives researchers the ability to remotely identify patterns of behaviour and calculate activity budgets.

Implications: Combining behaviour and activity budgets with location data provides useful insight into cockatoo movement, distribution, and habitat use. Such information is important for informing conservation efforts and addressing outstanding research objectives. Further studies including larger sample sizes of Baudin’s and forest red-tailed black cockatoos and comparing behaviour and activity between birds in breeding and non-breeding areas are warranted.

背景： Carnaby's ( Calyptorhychus latirostris )、Baudin's ( Calyptorhychus baudinii ) 和森林红尾黑凤头鹦鹉 ( Calyptorhynchus bankii naso ) 是澳大利亚西南部特有的濒危鹦鹉物种。行为监测以前涉及直接观察，由于其神秘的性质、它们穿过的栖息地类型以及它们的运动速度，这已被证明具有挑战性。从三轴加速度计数据中准确分类行为的模型的开发将为黑凤头鹦鹉行为和生态提供更深入的了解。

目标：开发一个自动分类器模型，对来自已发布的黑凤头鹦鹉的加速度计数据进行分类，以确定三种黑凤头鹦鹉的行为和活动预算。

方法：在本研究中，我们将安装在 GPS 标签中的三轴加速度计连接到四只卡纳比凤头鹦鹉、三只森林红尾黑凤头鹦鹉和两只被圈养照顾的 Baudin 凤头鹦鹉，这些凤头鹦鹉正在接受康复放归野外。这些鸟类的加速度计数据与鸟类飞行、进食和休息时的 19 个行为视频文件相结合，以开发自动行为分类器。然后使用分类器对成功康复后放归的 15 只鸡的加速度计数据进行注释，并计算这些鸡放归后的活动预算。

主要结果：我们开发了一个分类器，能够从加速度计数据中识别休息、飞行和觅食行为，准确率 86%，这取决于分类器正确识别的观察行为的百分比。将分类器应用于 15 只已发布的凤头鹦鹉的加速度计数据，使我们能够确定所有三种黑凤头鹦鹉的行为和活动预算。黑凤头鹦鹉大部分时间都在休息，其次是觅食和短时间飞行。

结论：将分类器应用于放生鸟类的数据使研究人员能够远程识别行为模式并计算活动预算。

意义：将行为和活动预算与位置数据相结合，可以提供对鹦鹉运动、分布和栖息地使用的有用见解。这些信息对于通知保护工作和解决突出的研究目标很重要。进一步的研究包括更大的 Baudin's 和森林红尾黑凤头鹦鹉样本量以及比较繁殖和非繁殖地区鸟类之间的行为和活动是有必要的。