Almeida-Ñauñay Andrés F, Benito Rosa María, Quemada Miguel, Losada Juan Carlos, Tarquis Ana M
Centro de Estudios e Investigación para la Gestión de Riesgos Agrarios y Medioambientales (CEIGRAM), Escuela Técnica Superior de Ingeniería Agronómica Alimentaria y de Biosistemas (ETSIAAB), Universidad Politécnica de Madrid, Senda del Rey, 13, 28040 Madrid, Spain.
Complex Systems Group, ETSIAAB, Universidad Politécnica de Madrid, Avda. Puerta de Hierro, no. 2., 28040 Madrid, Spain.
Entropy (Basel). 2021 Apr 30;23(5):559. doi: 10.3390/e23050559.
Multiple studies revealed that pasture grasslands are a time-varying complex ecological system. Climate variables regulate vegetation growing, being precipitation and temperature the most critical driver factors. This work aims to assess the response of two different Vegetation Indices (VIs) to the temporal dynamics of temperature and precipitation in a semiarid area. Two Mediterranean grasslands zones situated in the center of Spain were selected to accomplish this goal. Correlations and cross-correlations between VI and each climatic variable were computed. Different lagged responses of each VIs series were detected, varying in zones, the year's season, and the climatic variable. Recurrence Plots (RPs) and Cross Recurrence Plots (CRPs) analyses were applied to characterise and quantify the system's complexity showed in the cross-correlation analysis. RPs pointed out that short-term predictability and high dimensionality of VIs series, as well as precipitation, characterised this dynamic. Meanwhile, temperature showed a more regular pattern and lower dimensionality. CRPs revealed that precipitation was a critical variable to distinguish between zones due to their complex pattern and influence on the soil's water balance that the VI reflects. Overall, we prove RP and CRP's potential as adequate tools for analysing vegetation dynamics characterised by complexity.
多项研究表明,牧场草地是一个随时间变化的复杂生态系统。气候变量调节植被生长,其中降水和温度是最关键的驱动因素。这项工作旨在评估半干旱地区两种不同植被指数(VIs)对温度和降水时间动态的响应。为此,选择了位于西班牙中部的两个地中海草地地区。计算了植被指数与每个气候变量之间的相关性和交叉相关性。检测到每个植被指数系列的不同滞后响应,这些响应在不同地区、年份季节和气候变量中有所不同。应用递归图(RPs)和交叉递归图(CRPs)分析来表征和量化交叉相关性分析中显示的系统复杂性。递归图指出,植被指数系列以及降水的短期可预测性和高维性是这种动态的特征。与此同时,温度呈现出更规则的模式和更低的维度。交叉递归图显示,降水是区分不同地区的关键变量,这是由于其复杂的模式以及对植被指数所反映的土壤水分平衡的影响。总体而言,我们证明了递归图和交叉递归图作为分析具有复杂性特征的植被动态的合适工具的潜力。