The use of miniature accelerometer (ACT) data-loggers for remote and continuous recording of animal movement behavior is becoming increasingly common. Until recently, size constraints limited most animal-borne ACT applications to large-bodied species. We capitalized on the ongoing miniaturization and advancement of these technologies and associated computational techniques to develop a framework for long-term, low-frequency ACT monitoring of activity in small and secretive terrestrial species. We achieved this by internally implanting coupled radio transmitters and tri-axial ACTs in rattlesnakes (Crotalus atrox). Periodic field-validation observations of behavior were used to train and test supervised learning models (Random Forest, RF; Generalized Linear Elastic Net, GLMNET) for activity classification. The best performing RF model classified periods of movement and immobility in rattlesnakes with high accuracy (movement = 96%, immobile = 99%), and was applied to extensive ACT field datasets (median = 35 days, range = 6–289 days; N = 12) to produce activity budgets at multiple temporal scales. In general, these cryptic ambush predators were found to be highly sedentary, with activity budgets characterized by long periods of immobility interrupted by punctuated bouts of movement. The same temporal daily activity pattern was conserved across all active seasons (spring, summer non-mating, summer mating, fall), as most movement always occurred during the evening or nocturnal diel periods. Contrary to movement timing, daily movement duration was seasonally variable, as movement increased during the summer-mating season—possibly reflecting a combination of more favorable weather conditions (onset of rainy season) and mate-searching efforts by male rattlesnakes. This radio telemetry-accelerometry (RT-ACT) framework provides an objective and flexible set of data collection and processing procedures for long-term ACT field datasets. Expanding our coarse-scale behavioral classification scheme could provide a foundation for future investigations using the RT-ACT framework to explore relationships between individual behavioral decisions and performance in snakes and other small and secretive terrestrial vertebrates.

中文翻译：

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小型和秘密动物长期活动监测的综合框架：用隐秘的蝰蛇验证

使用微型加速度计 (ACT) 数据记录器远程和连续记录动物运动行为正变得越来越普遍。直到最近，尺寸限制仍将大多数动物传播的 ACT 应用限制在体型较大的物种上。我们利用这些技术和相关计算技术的持续小型化和进步，开发了一个框架，用于长期、低频 ACT 监测小型和秘密陆地物种的活动。我们通过在响尾蛇 (Crotalus atrox) 内部植入耦合无线电发射器和三轴 ACT 来实现这一点。行为的定期现场验证观察被用于训练和测试监督学习模型（随机森林，RF；广义线性弹性网络，GLMNET）以进行活动分类。性能最佳的 RF 模型以高精度对响尾蛇的运动和静止期进行分类（运动 = 96%，静止 = 99%），并应用于广泛的 ACT 现场数据集（中位数 = 35 天，范围 = 6-289 天；N = 12) 在多个时间尺度上产生活动预算。总的来说，这些神秘的伏击捕食者被发现是高度久坐不动的，其活动预算的特点是长时间的不动被间断的运动打断。在所有活跃季节（春季、夏季非交配、夏季交配、秋季）中都保留了相同的时间性日常活动模式，因为大多数运动总是发生在晚上或夜间活动期间。与运动时间相反，每日运动持续时间随季节变化，随着夏季交配季节活动的增加——可能反映了更有利的天气条件（雨季开始）和雄性响尾蛇寻找配偶的努力。这种无线电遥测加速度计 (RT-ACT) 框架为长期 ACT 现场数据集提供了一套客观且灵活的数据收集和处理程序。扩展我们的粗尺度行为分类方案可以为未来使用 RT-ACT 框架的调查提供基础，以探索蛇和其他小型和秘密陆生脊椎动物的个体行为决策与表现之间的关系。这种无线电遥测加速度计 (RT-ACT) 框架为长期 ACT 现场数据集提供了一套客观且灵活的数据收集和处理程序。扩展我们的粗尺度行为分类方案可以为未来使用 RT-ACT 框架的调查提供基础，以探索蛇和其他小型和秘密陆生脊椎动物的个体行为决策与表现之间的关系。这种无线电遥测加速度计 (RT-ACT) 框架为长期 ACT 现场数据集提供了一套客观且灵活的数据收集和处理程序。扩展我们的粗尺度行为分类方案可以为未来使用 RT-ACT 框架的调查提供基础，以探索蛇和其他小型和秘密陆生脊椎动物的个体行为决策与表现之间的关系。