Nonparametric upscaling of stochastic simulation models using transition matrices
Nonparametric upscaling of stochastic simulation models using transition matrices
复制标题
使用转移矩阵的随机仿真模型的非参数升级
DOI：
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发表时间：
2016
期刊：
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通讯作者：
J. Paruelo
中科院分区：
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作者：
P. Cipriotti;T. Wiegand;Sandro Pütz;Norberto J. Bartoloni;J. Paruelo
从易于处理的小规模观测和实验到大规模模式预测的问题是生态学理论和应用的核心，也是生态学的核心问题之一。我们提出并测试了一个通用的非参数框架，以提升空间显式和随机模拟模型。其思想是设计一个由小尺度模型的重要状态变量定义的状态空间，并将其划分为有限个离散状态。然后通过监测小规模模型的广泛模拟运行来统计转移概率，涵盖所需应用可能出现的整个初始条件，状态和外部驱动因素。我们通过放大一个基于个体的模型来扩展我们的方法，该模型模拟了阿根廷巴塔哥尼亚西部绵羊放牧下的羊茅草原的时空动态，空间分辨率为0.3 m × 0.3 m，范围为0.15公顷。放大模型模拟了一个2500公顷的围场，分辨率为0·15公顷，并增加了额外的规则，描述了围场规模的当地放牧率的异质性。我们得到了24个过渡矩阵，管理不同组合的载畜率和年降水量的放大模型。升级后的模式对长期动态做出了很好的预测，但正如预期的那样，它没有完全捕捉到原始模式的年际动态。当地放牧率的异质性规则允许出现现实的植被格局，如干旱牧场的供水点通常观察到的那样。我们的一般非参数尺度放大方法可以应用于广泛的随机模拟模型，其中的动态可以近似由一组状态，转换和外部驱动程序。由于转移概率的估计可以并行进行，因此我们的方法可以应用于中等复杂度的各种模型。我们的方法弥补了我们从生物学知识可用的小规模扩大到与管理相关的更大规模的能力方面的差距。
The problem of scaling up from tractable, small‐scale observations and experiments to prediction of large‐scale patterns is at the core of ecological theory and application, and one of the central problems in ecology. We present and test a general nonparametric framework to upscale spatially explicit and stochastic simulation models. The idea is to design a state space, defined by the important state variables of the small‐scale model, and to divide it into a finite number of discrete states. Transition probabilities are then tallied by monitoring extensive simulation runs of the small‐scale model, covering the entire range of initial conditions, states and external drivers that may occur for the desired application. We exemplify our approach by upscaling an individual‐based model that simulates the spatiotemporal dynamics of Festuca pallescens steppes under sheep grazing in Western Patagonia, Argentina, with a spatial resolution of 0·3 m × 0·3 m and a 0·15‐ha extent. The upscaled model simulates a 2500‐ha paddock with 0·15‐ha resolution and is enriched with additional rules that describe heterogeneity in the local stocking rate at the paddock scale. We obtained 24 transition matrices that governed the upscaled model for different combinations of stocking rates and annual precipitation. The upscaled model produced excellent predictions for the long‐term dynamics, but as expected, it did not fully capture the interannual dynamics of the original model. Rules for heterogeneity in the local stocking rate allowed for emergence of realistic vegetation patterns as commonly observed for water points in arid rangelands. Our general nonparametric upscaling approach can be applied to a wide range of stochastic simulation models in which the dynamics can be approximated by a set of states, transitions and external drivers. Because estimation of the transition probabilities can be done parallel, our approach can be applied to a wide range of models of intermediate complexity. Our approach closes a gap in our ability to scale up from small scales, where the biological knowledge is available, to larger scales that are relevant for management.