Variability in multicomponent ecological systems: a test of new framework

多组分生态系统的变异性：新框架的测试

• 批准号: RGPIN-2015-04601
• 负责人: Kolasa, Jurek
• 金额: $ 1.53万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=613057
• 关键词: Variability; multicomponent; ecological; systems; test

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.

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，害虫爆发的频率，物种随时间的损失，我们可以将其与该变量在空间上的变化完全联系起来。更准确地说，当站点独立变化时，随时间变化的方差等于空间变化的方差，如果站点一致变化，则方差可预测地更大，如果站点之间的变化始终不同，则方差可预测地更小。发现的关系提供了一般的预测和诊断的机会，评估感兴趣的生态因素的变化，重要的是，这种变化的机制。通过理论分析，实验室生态系统，自然水生微宇宙，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，该模型允许从空间变化（通常可从调查，公共记录，地图或卫星图像中获得）预测时间（通常很难获得）。由于种群的变异性决定了灭绝风险、收获水平和管理选择;而生态系统服务的变异性影响着我们的健康、娱乐和当地经济，因此仅从空间格局进行评估的能力是有利的。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，我们建议对该模型进行几项测试： (1)它的基本期望-我们将通过实验室实验测试关于栖息地连通性和环境变化对潜在方程预测能力的影响的假设; (2)模型所揭示的模式本质上是重要的。我们将通过在一个小型天然池塘系统中操纵连通性和栖息地变异性来做到这一点; (3)通过进行“概念验证”探索发现的实际意义-在这里，我们将把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，以确定它们是否可以从农业生态系统生态属性的空间变化或异质性中预测，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。由于偏离模型的“默认”或零预测是诊断性的，这些属性的空间变化和每种作物的变化之间的特定联系将使我们能够确定潜在的机制。这应有助于揭示区域（按大陆或次大陆）粮食生产的脆弱性及其决定因素。因此，这种研究可以协助有关国际和国家机构评估和调整其政策，并与养护目标和政策形成更知情的互动。