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，例如人口规模、森林生产力、害虫爆发频率、物种损失随时间的变化，我们可以将其与该变量在空间上的变化准确地联系起来，更准确地说，是方差。当地点独立变化时，随时间的变化在空间上相等；如果它们一致变化，则可预见地更大；如果地点之间的差异始终不同，则可预见地较小。所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，并且很重要。 , 潜在机制通过理论分析、实验室生态系统、自然水生微观世界和作物产量的实验，我们建议测试这种关系的合理性和实用性。 最有用的是，该模型允许根据空间变化（通常可通过调查、公共记录、地图或卫星图像获得）来预测时间（通常很难获得），因为种群的变异性决定了灭绝风险、收获水平以及管理选项和变异性。生态系统服务影响着我们的健康、娱乐和当地经济，仅从空间模式对其进行评估的能力是有利的，将环境的变化与生物过程和物种的变化联系起来是我的长期目标。对模型进行多次测试： （1）其基本期望——我们将通过实验室实验来检验有关栖息地连通性和环境变化对基础方程预测能力影响的假设； (2) 模型提出的模式在本质上很重要，我们将通过操纵小型天然池塘系统的连通性和栖息地变异性来实现这一点； (3) 通过进行“概念验证”探索得出的实际意义 - 在这里，我们将将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来确定它们是否可以。可以根据农业生态系统的空间变化或异质性进行预测，例如降水量、栖息地破碎化、化肥使用、当地经济指标、交通网络等，因为偏离模型的“默认”或无效预测是诊断性的，具体的。空间之间的联系这些属性的变化和每种作物的变化将使我们能够确定潜在的机制，这将有助于揭示区域（按大陆或次大陆）粮食生产的脆弱性及其决定因素，从而帮助相关国际和国家机构进行评估和调整。他们的政策以及与保护目标和政策建立更明智的互动。