Variability in multicomponent ecological systems: a test of new framework

最近我们发现，如果我们测量生态变量的变化，如人口规模、森林生产力、虫害爆发频率、物种损失随时间的变化，我们可以准确地将其与该变量在空间上的变化联系起来。更准确地说，当地点独立变化时，随着时间的变化等于跨越空间的变化，如果它们一致变化，可预测的变化会更大，如果它们的变化在地点之间持续不同，可预测的变化会更小。发现的关系为评估感兴趣的生态因素的变化提供了一般的预测和诊断机会，更重要的是，这种变化的机制。通过理论分析、实验室生态系统、天然水生微生物和作物产量的实验，我们建议测试这种关系的合理性和实用性。最有用的是，该模型可以从空间变化（通常可以从调查、公共记录、地图或卫星图像中获得）中预测时间（通常很难获得）。因为种群的可变性决定了灭绝风险、收获水平和管理方案；生态系统服务的可变性影响着我们的健康、娱乐和当地经济，仅从空间模式来评估它的能力是有利的。将环境的可变性与生物过程和物种的可变性联系起来是我的长期目标。在短期内，我们建议对模型进行以下几方面的检验：***(1)其基本期望——我们将通过实验室实验检验关于栖息地连通性和环境变化对基础方程预测能力的影响的假设；***(2)模型提出的模式在本质上是重要的。我们将通过控制小型天然池塘系统的连通性和栖息地变化来实现这一目标；***(3)通过“概念验证”探索发现的实际意义——在这里，我们将把该模型应用于全球粮食生产。我们将使用联合国关于世界上15种最重要作物的数据，以确定是否可以从农业生态系统的生态属性（如降水量、栖息地破碎化、肥料使用、当地经济指标、运输网络等）的空间变化或异质性来预测它们。由于偏离模型的“默认”或零预测是诊断性的，因此这些属性的空间变化与每种作物的变化之间的特定联系将使我们能够识别潜在的机制。这应有助于揭示区域（按大陆或次大陆划分）粮食生产及其决定因素的脆弱性。因此，这种研究可以帮助有关的国际和国家机构评估和调整其政策，并与保护目标和政策建立更明智的相互作用。**

Recently we discovered that if we measure variation in an ecological variables such as population size, productivity of a forest, frequency of pest a outbreak, species loss over TIME, we can relate it EXACTLY to variation of this variable over SPACE. More precisely, variance over time equals variance across space when sites vary independently, is predictably greater if they vary in concert and predictably less if their variation is consistently different between sites. The discovered relationship offers general PREDICTIVE and DIAGNOSTIC opportunities for assessing variation of ecological factors of interest and, importantly, mechanisms underlying this variation. Through theoretical analyses, experiments with laboratory ecosystems, natural aquatic microcosms, and crop yields we propose to test soundness and utility of this relationship. ***Most usefully, the model allows predicting temporal (often hard to get) from spatial variation (often available from surveys, public records, maps, or satellite images). Because variability of populations defines extinction risks, harvest levels, and management options; and variability of ecosystem services affects our health, recreation, and local economies, ability to assess it from spatial patterns alone is advantageous. Linking variability of environment to that of biological processes and species is my long-term goal. In the short term, we propose to conduct several tests of the model: ***(1) Its basic expectations - we will test hypotheses about the effects of habitat connectivity and environmental variation on the predictive power of the underlying equation via lab experiments; ***(2) That the patterns suggested by the model are important in nature. We will do so by manipulating connectivity and habitat variability in a system of small natural ponds; ***(3) The practical significance of findings by performing `proof-of-concept' exploration - here, we will apply the model to global food production. We will use UN data on the 15 most important crops of the world to determine if they can be predicted from spatial variation or heterogeneity in ecological attributes agroecosystems such as precipitation amount, habitat fragmentation, fertilizer use, local economic metrics, transport network, and others. Because departures from the `default' or null predictions of the model are diagnostic, the specific links between spatial variation of these attributes and variation of each crop will permit us to identify underlying mechanisms. This should help uncover vulnerabilities in regional (by continent or subcontinent) food production and its determinants. Such research could thus assist relevant international and national agencies in evaluating and adjusting their policies as well as forge more informed interactions with conservation goals and policies. **