"Spatial population processes, global drivers, and positive interactions: critical lacunae in ecological forecasting"

“空间人口过程、全球驱动因素和积极的相互作用：生态预测的关键缺陷”

• 批准号: 327415-2012
• 负责人: McIntire, Eliot
• 金额: $ 1.89万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=506930
• 关键词: Spatial; population; processes; global; drivers

Ecosystems and landscapes are being affected by anthropogenic changes to habitats, climate, and species composition. Some species will be resilient to these changes, and others will change or disappear. The task of applied ecology has been to predict these changes by integrating knowledge and theory across disciplines to answer society's needs. Along the way, new quantitative approaches have been required that correctly account for the spatio-temporal nature of the empirical data available to support decisions and to quantify prediction uncertainty. In this research, I propose to forecast populations in four dynamic ecological systems: woodland caribou and mountain pine beetle (MPB) in the boreal forest of Canada, wolves in the Western US, and alpine treelines in 3 continental mountain ranges. These systems are experiencing dramatic range and population changes through expansions (MPB, wolves, treeline) or contractions (caribou). These changes are driven by a complex of factors in flux that include climate (e.g., MPB and treeline), habitat degradation and increased predation (caribou), and severe hunting restrictions during reintroduction that have now been removed (wolves). Accurate forecasts of the evolution of these systems will have potentially large direct economic impacts (e.g. on the pine forestry resource in boreal Canada and woodland caribou recovery plans) and will contribute to the conservation of endangered species (caribou, wolves and others). I will use an Occam's Razor inspired approach to integrating diverse ecological and applied fields in a spatially explicit, individual-based modeling framework. To be able to develop credible spatially dynamic models, I have identified three critical areas of uncertainty that require more detailed empirical analyses: dispersal through complex landscapes, distinguishing global from the local drivers that may allow management actions, and the potential for positive interactions among individuals within the same trophic level that can markedly alter system dynamics. The combination of advances in understanding of these detailed ecological phenomena and the integration of these with prior studies will allow me to best forecast species within the intact, but changing systems.

生态系统和景观正受到生境、气候和物种组成的人为变化的影响。一些物种将适应这些变化，而其他物种将改变或消失。应用生态学的任务是通过整合跨学科的知识和理论来预测这些变化，以满足社会的需求。在此过程中，需要新的定量方法来正确地解释可用于支持决策和量化预测不确定性的经验数据的时空性质。在本研究中，我提出了四个动态生态系统的种群预测：加拿大北方森林的林地驯鹿和山松甲虫（MPB），美国西部的狼，以及3个大陆山脉的高山树线。这些系统正经历着巨大的范围和数量变化，通过扩张（MPB，狼，树线）或收缩（驯鹿）。这些变化是由一系列不断变化的因素驱动的，包括气候（例如，MPB和树线）、栖息地退化和捕食增加（驯鹿），以及在重新引入期间已经取消的严格狩猎限制（狼）。对这些系统演变的准确预测将产生潜在的巨大直接经济影响（例如，对加拿大北部的松林资源和林地驯鹿恢复计划），并将有助于保护濒危物种（驯鹿、狼和其他物种）。我将使用奥卡姆剃刀启发的方法，将不同的生态和应用领域整合到一个空间明确的、基于个体的建模框架中。为了建立可信的空间动态模型，我确定了三个关键的不确定性领域，这些领域需要更详细的实证分析：通过复杂景观的分散，区分可能允许管理行动的全球驱动因素和局部驱动因素，以及在同一营养水平上个体之间可能显著改变系统动态的积极互动的潜力。结合对这些详细生态现象的理解，并将其与先前的研究相结合，将使我能够最好地预测完整但不断变化的系统中的物种。