Anuran Calling Survey Optimization: Developing and Testing Predictive Models of Anuran Calling Activity
Anuran Calling Survey Optimization: Developing and Testing Predictive Models of Anuran Calling Activity
复制标题
阿努拉叫声调查优化：开发和测试阿努拉叫声活动的预测模型
DOI：
10.1670/08-329.1
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
M. Dorcas
中科院分区：
文献类型：
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作者：
C. Steelman;M. Dorcas
摘要两栖动物，特别是无尾两栖动物的数量在世界范围内正在下降，利用呼叫调查的程序已经建立起来，以监测无尾两栖动物的数量。描述环境对呼叫的影响的模型可能有助于增加检测，从而允许优化调查。使用自动记录系统，我们评估了呼叫活动的十字花科，Pseudacris feriarum，蛙sphenocephala在短暂的湿地在皮埃蒙特的北卡罗来纳州。我们使用逐步逻辑回归模型的环境变量，显着影响呼叫活动。模型显示，对于P.十字花科，水和空气温度的积极影响调用，而一年中的一天，气压和光照强度的负面影响调用。温度对雌蜂的鸣叫有正向影响，而天数、相对湿度、风速、气压和光照强度对雌蜂的鸣叫有负向影响。最后，气温对R的呼唤有积极的影响。sphenocephala，而水温，相对湿度，风速和光照强度的负面影响其调用。利用这些结果，我们开发了全面的，以及更简单的，“用户友好”的模型，预测最佳条件下进行无尾两栖动物呼叫调查。用户友好的模型进行了测试，使用以前收集的数据，从呼叫调查在同一地区的北卡罗来纳州，发现准确预测呼叫活动的时间约70%。我们将讨论如何将天气预报数据应用于这些模型，以确定进行呼叫调查的最佳时间，以及如何使用本研究中开发的模型来解释先前在两栖动物呼叫调查期间收集的数据。
Abstract Amphibian populations, particularly anurans, are declining worldwide, and programs that use calling surveys have been established to monitor anuran populations. Models that describe the environment's influence on calling may be useful to increase detection allowing optimization of surveys. Using an automated recording system, we evaluated the calling activity of Pseudacris crucifer, Pseudacris feriarum, and Rana sphenocephala at an ephemeral wetland in the Piedmont of North Carolina. We used stepwise logistic regression to model environmental variables that significantly affected calling activity. Models revealed that, for P. crucifer, water and air temperature positively influenced calling, whereas day of year, barometric pressure, and light intensity negatively influenced calling. For P. feriarum, air temperature positively influenced calling, and day of year, relative humidity, wind speed, barometric pressure, and light intensity negatively influenced calling. Finally, air temperature positively influenced calling for R. sphenocephala, whereas water temperature, relative humidity, wind speed, and light intensity negatively influenced its calling. Using these results, we developed comprehensive as well as simpler, “user-friendly” models, predicting the best conditions under which to conduct anuran calling surveys. The user-friendly models were tested using previously collected data from calling surveys performed in the same region of North Carolina and found to accurately predict calling activity approximately 70% of the time. We discuss how weather forecast data may be applied to these models to determine the best times to conduct calling surveys and how models such as those developed in this study can be used to interpret data previously collected during amphibian calling surveys.