Galante Julia, Margulis Susan W
Department of Animal Behavior, Ecology, and Conservation, Canisius College, Buffalo, NY 14208, USA.
Department of Biology, Canisius College, Buffalo, NY 14208, USA.
Animals (Basel). 2022 Nov 3;12(21):3031. doi: 10.3390/ani12213031.
Systematic data collection has become increasingly important in zoos as it facilitates evidence-based decision-making. Here, we describe the results of a two-year study on exhibit use and pair-bonding in a colony of Humboldt penguins. We used two different data collection apps to evaluate their effectiveness and suitability for evaluating pair-bond strength. Data were collected using instantaneous scan sampling and all-occurrence sampling 2-3 times per week for 2 years for a total of nearly 240 h of observation (19 h with one system and 219 h with the other system). The activity patterns (in particular, time spent in the water) differed amongst penguins and between the two data collection tools. Patterns of courtship-related behaviors varied tremendously across individuals. The longer pairs had been bonded, the more time they spent in close proximity. We highlight two important considerations for institutions aiming to collect such systematic data. First, it is critical to interpret all findings in context by incorporating husbandry details and keeper insights to highlight explanations that may not be readily apparent from the data. Second, one must explore all aspects of any data collection system before committing to its use-system setup, ease of data collection, format and accessibility of exported data. Not doing so may negate the value of systematic data collection by limiting the use and interpretability of the data.
在动物园中，系统的数据收集变得越来越重要，因为它有助于基于证据的决策。在此，我们描述了一项针对洪堡企鹅群体的展览使用情况和配对关系的为期两年的研究结果。我们使用了两款不同的数据收集应用程序来评估它们在评估配对强度方面的有效性和适用性。数据收集采用即时扫描抽样和全事件抽样，每周2 - 3次，持续2年，总共进行了近240小时的观察（使用一个系统观察了19小时，使用另一个系统观察了219小时）。企鹅之间以及两种数据收集工具之间的活动模式（特别是在水中的时间）存在差异。求偶相关行为的模式在个体之间差异极大。配对时间越长，它们彼此靠近的时间就越多。我们强调了对于旨在收集此类系统数据的机构的两个重要考虑因素。首先，通过纳入饲养细节和饲养员的见解来结合背景解读所有发现至关重要，以突出那些从数据中可能不容易看出的解释。其次，在致力于使用任何数据收集系统之前，必须探索其各个方面——系统设置、数据收集的难易程度、导出数据的格式和可访问性。不这样做可能会因限制数据的使用和可解释性而否定系统数据收集的价值。
