使用移除数据和分层模型估计入侵啮齿动物丰度
Estimating invasive rodent abundance using removal data and hierarchical models
AI总结：
针对入侵啮齿动物丰度估计的传统方法局限，提出应用分层多项N-混合模型处理移除数据，通过模拟验证模型可靠性，结合实际数据展示其在分析与应对假设违反中的价值，为入侵物种管理提供方法支持。
摘要：
入侵啮齿动物带来重大的生态、经济和公共卫生挑战。需要可靠的种群丰度估计方法来指导有效管理。由于伦理、法律和后勤限制，传统的捕获-再捕获等方法对入侵物种往往不切实际。本文展示了分层多项N-混合模型在利用移除数据估计入侵啮齿动物丰度中的应用。首先，作者进行模拟研究，表明在多种采样场景下，该模型偏差极小，且精度良好、置信区间覆盖可靠。同时展示了违反种群封闭性假设的后果，即场合间动态如何导致推断偏差。其次，分析法国海狸鼠和荷兰麝鼠两种入侵啮齿动物的移除数据，用分层多项N-混合模型检验温度对丰度的影响，同时考虑不完全且时变的捕获概率；还展示了如何用随机效应处理空间变异性、量化参数估计的不确定性，以及用开放种群模型拟合多年数据来应对封闭性假设的违反。总体而言，本文旨在证明分层模型在入侵物种管理中的灵活性和实用性。
英文摘要：
Invasive rodents pose significant ecological, economic, and public health challenges. Robust methods are needed for estimating population abundance to guide effective management. Traditional methods such as capture-recapture are often impractical for invasive species due to ethical, legal and logistical constraints. Here, I showcase the application of hierarchical multinomial N-mixture models for estimating the abundance of invasive rodents using removal data. First, I perform a simulation study which demonstrates minimal bias, as well as good precision and reliable coverage of confidence intervals across a range of sampling scenarios. I also illustrate the consequences of violating the population closure assumption, showing how between-occasion dynamics can bias inference. Second, I analyze removal data for two invasive rodent species, namely coypus (Myocastor coypus) in France and muskrats (Ondatra zibethicus) in the Netherlands. Using hierarchical multinomial N-mixture models, I examine the effects of temperature on abundance while accounting for imperfect and time-varying capture probabilities. I also show how to accommodate spatial variability using random effects, quantify uncertainty in parameter estimates, and account for violations of closure by fitting an open-population model to multi-year data. Overall, I hope to demonstrate the flexibility and utility of hierarchical models in invasive species management.
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