Identifying bat roost locations through the coupling of motion modelling and microphone field data.

为了提高对蝙蝠生态的认识，实现更有效的保护，经常有必要在蝙蝠聚集的地方寻找栖息地，有时是大量栖息。不幸的是，对于生态顾问和研究科学家来说，寻找栖息地，甚至确定某个地区是否可能存在栖息地都是极其困难的。具体地说，现场数据收集既昂贵又耗时。为预测创建一个理论框架，可以在特定地点使用较少的实地数据进行验证，这是一种新颖而受欢迎的方法。为了更好地了解蝙蝠在景观中的斑块分布和最大限度地成功定位栖息地，我们建议将Fiona Mathews教授(FM)的生态学专业知识与Thomas Woolley博士(TW)和Owen Jones教授(OJ)的建模技巧结合起来。FM和TW已经合作制作了一份初步分析报告，表明使用声学调查记录蝙蝠的可能性强烈依赖于栖息地的距离。为了巩固这方面的知识并扩展研究结果，学生将：*创建蝙蝠种群的随机随机运动模型；*使用现场数据将模型参数化；*模拟并提供蝙蝠空间传播和栖息位置的分析预测；*使用信息论估计蝙蝠检测的最佳麦克风数量(基于优化现场工作人员的努力、误差容限和财务约束)及其空间传播；*根据真实环境数据调整和改进模型以包括有偏的运动；*生物学家基于他们选择的可变参数提供上述问题的即时解决方案的代码软件；工作计划有一个结构良好的学习曲线，因为许多洞察力可以从简单的随机模型中获得。随着学生信心的增强，我们将寻求包括更复杂的因素，如环境异质性和定向运动，以使我们的结果更现实。

To improve knowledge of bat ecology and achieve more effective conservation, it is frequently necessary to locate roosts where the animals congregate, sometimes in large numbers. Unfortunately, finding roosts, or even determining whether a roost is likely to be present in a given area, is extremely difficult for both ecological consultants and research scientists. Specifically, field data collection is expensive and time consuming. Creating a theoretical framework for predictions, which can be validated in particular locations using smaller amounts of field data, is a novel and welcomed approach. To understand better the patchy distribution of bats in the landscape and to maximise the success of locating roosts, we propose to combine the ecological expertise of Prof. Fiona Mathews (FM) and the modelling skills of Dr Thomas Woolley (TW) and Prof. Owen Jones (OJ). FM and TW have already worked together to produce a preliminary analysis showing that the probability of recording bats using acoustic surveys is strongly dependent on roost proximity. In order to consolidate this knowledge and extend the results this studentship will: * create a stochastic random motion model of the bat population; * parameterise the model using field data; * simulate and provide analytical predictions of bat spatial spread and roost location; * use information theory to estimate the optimal number of microphones (based on optimising field worker effort, error tolerances and financial constraints) and their spatial spread for bat detection; * adapt and refine the model to include biased motion depending on real environmental data; * code software for the biologists to provide immediate solutions to the problems above based on variable parameters that they choose; The program of work has a well-structured learning curve in that many insights can be gained from simple stochastic models. As the student's confidence increases, we will seek to include further complicating factors, such as environmental heterogeneity and directed motion, to provide more realism in our results.