Franch-Gras Lluis, García-Roger Eduardo Moisés, Franch Belen, Carmona María José, Serra Manuel
Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Universitat de València, Valencia, Spain.
NASA Goddard Space Flight Center, Greenbelt, MD, United States of America.
PLoS One. 2017 Nov 9;12(11):e0187958. doi: 10.1371/journal.pone.0187958. eCollection 2017.
Fluctuations in environmental parameters are increasingly being recognized as essential features of any habitat. The quantification of whether environmental fluctuations are prevalently predictable or unpredictable is remarkably relevant to understanding the evolutionary responses of organisms. However, when characterizing the relevant features of natural habitats, ecologists typically face two problems: (1) gathering long-term data and (2) handling the hard-won data. This paper takes advantage of the free access to long-term recordings of remote sensing data (27 years, Landsat TM/ETM+) to assess a set of environmental models for estimating environmental predictability. The case study included 20 Mediterranean saline ponds and lakes, and the focal variable was the water-surface area. This study first aimed to produce a method for accurately estimating the water-surface area from satellite images. Saline ponds can develop salt-crusted areas that make it difficult to distinguish between soil and water. This challenge was addressed using a novel pipeline that combines band ratio water indices and the short near-infrared band as a salt filter. The study then extracted the predictable and unpredictable components of variation in the water-surface area. Two different approaches, each showing variations in the parameters, were used to obtain the stochastic variation around a regular pattern with the objective of dissecting the effect of assumptions on predictability estimations. The first approach, which is based on Colwell's predictability metrics, transforms the focal variable into a nominal one. The resulting discrete categories define the relevant variations in the water-surface area. In the second approach, we introduced General Additive Model (GAM) fitting as a new metric for quantifying predictability. Both approaches produced a wide range of predictability for the studied ponds. Some model assumptions-which are considered very different a priori-had minor effects, whereas others produced predictability estimations that showed some degree of divergence. We hypothesize that these diverging estimations of predictability reflect the effect of fluctuations on different types of organisms. The fluctuation analysis described in this manuscript is applicable to a wide variety of systems, including both aquatic and non-aquatic systems, and will be valuable for quantifying and characterizing predictability, which is essential within the expected global increase in the unpredictability of environmental fluctuations. We advocate that a priori information for organisms of interest should be used to select the most suitable metrics for estimating predictability, and we provide some guidelines for this approach.
环境参数的波动日益被视为任何栖息地的基本特征。环境波动是普遍可预测还是不可预测的量化，对于理解生物体的进化反应非常重要。然而，在描述自然栖息地的相关特征时，生态学家通常面临两个问题：（1）收集长期数据；（2）处理来之不易的数据。本文利用免费获取的长期遥感数据记录（27年，陆地卫星TM/ETM+）来评估一组用于估计环境可预测性的环境模型。案例研究包括20个地中海咸水池塘和湖泊，重点变量是水面面积。本研究首先旨在开发一种从卫星图像准确估计水面面积的方法。咸水池塘会形成盐壳区域，这使得区分土壤和水变得困难。通过一种结合波段比值水指数和短波近红外波段作为盐过滤器的新颖流程解决了这一挑战。然后，该研究提取了水面面积变化中的可预测和不可预测成分。使用两种不同的方法（每种方法在参数上都有变化）来获得围绕规则模式的随机变化，目的是剖析假设对可预测性估计的影响。第一种方法基于科尔韦尔的可预测性指标，将重点变量转换为名义变量。由此产生的离散类别定义了水面面积的相关变化。在第二种方法中，我们引入广义相加模型（GAM）拟合作为量化可预测性的新指标。两种方法都为所研究的池塘产生了广泛的可预测性范围。一些模型假设——先验地被认为非常不同——影响较小，而其他一些假设产生的可预测性估计则显示出一定程度的差异。我们假设这些可预测性的不同估计反映了波动对不同类型生物体的影响。本手稿中描述的波动分析适用于各种系统，包括水生和非水生系统，对于量化和表征可预测性将是有价值的，而可预测性在预期的全球环境波动不可预测性增加的情况下至关重要。我们主张应使用感兴趣生物体的先验信息来选择最适合估计可预测性的指标，并为此方法提供一些指导方针。
