Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover
Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover
复制标题
了解非线性种群轨迹的不确定性：大规模珊瑚覆盖调查的贝叶斯半参数分层方法
作者：
Yong Deng;Julie Vercelloni;M. Caley;Mohsen Kayal;S. Low;K. Mengersen
最近，试图改善物种管理的决策集中在与模拟种群的时间波动相关的不确定性。减少模型的不确定性是具有挑战性的;虽然较大的样本可以改善物种轨迹的估计并减少统计误差，但它们通常会放大观测轨迹的可变性。特别是，旨在估计人口轨迹的传统建模方法通常不能很好地解释与大型时空调查的多尺度观测特征相关的非线性和不确定性。我们提出了一个贝叶斯半参数层次模型，同时量化与模型结构和参数相关的不确定性，以及随时间推移的尺度特异性变异。我们估计大堡礁珊瑚覆盖的四层空间层次结构的不确定性。珊瑚的变异性得到很好的描述，但是，我们的研究结果表明，在没有额外的模型规格，珊瑚轨迹的结论变得高度不确定时，考虑多个珊瑚礁，这表明管理应更多地集中在单个珊瑚礁的规模。所提出的方法便于描述和估计人口的轨迹和相关的不确定性时，变异不能归因于特定的原因和起源。我们认为，我们的模型可以释放大规模数据集中包含的价值，为理解不确定性的来源提供指导，并支持更明智的决策。
Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making.