Optimizing sampling design, data integration, and methods for understanding population dynamics and predicting ecological changes

优化抽样设计、数据整合以及了解种群动态和预测生态变化的方法

• 批准号: RGPIN-2020-07034
• 负责人: Hamel, Sandra
• 金额: $ 2.77万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=745010
• 关键词: Optimizing; sampling; design; data; integration

My long-term objective is to evaluate, develop and apply analytical methods to quantify ecological processes describing the interplay among biotic, abiotic, and anthropogenic drivers of population and ecosystem dynamics, particularly in northern regions. Given the current drastic ecological changes, improvements in our ability to predict the dynamics of ecological systems are urgently needed. The development of reliable predictive tools is indispensable to better anticipate climate-induced impacts on ecosystems. My short-term research objectives seek to 1) improve sampling designs and methods for better integrating multiple data sources to model drivers of population and ecosystem dynamics, 2) foster new methods to combine indigenous and scientific knowledge to enhance our understanding of ecological changes in the North, and 3) develop models to produce near-term (seasonal/annual) iterative forecasts to better anticipate population and ecosystem changes and adapt management actions in real time. My program is embedded within the Near-Term Iterative Forecasting (NTIF) framework. NTIF uses models integrating data from diverse sources and temporal scales (day, season, year) to describe ecosystem responses. It then produces daily to annual scale forecasts that are updated as new data become available. Building upon several ongoing long-term studies, including some that have already collected extensive data on state variables and drivers, my group will advance knowledge at all steps required to reach useful ecological forecasts. We will assess how study designs combining new alternative methods (camera-traps, individual identification from hair DNA, Citizen Science sampling) can reduce the uncertainty of estimates and improve the efficiency of data collection at large spatiotemporal scales. We will develop process-oriented models, combining multiple data sources to unravel which mechanisms drive ecological changes. For instance, we will test whether climatic or anthropogenic processes regulate black bear population dynamics. We will develop robust analytical methods to model qualitative data, allowing us to use indigenous knowledge to test the hypothesis that climate-induced mechanisms synchronize population dynamics among key Arctic species. We will develop NTIF models and evaluate their limitations for predicting future ecosystem states. These models will examine climate impacts on the northward spread of winter ticks (Québec) and defoliating moths (Norway), and their respective consequences on moose populations and birch forests dynamics (mortality, regeneration). My program will train innovative HQP who will accelerate scientific learning by shifting from the current focus on measuring impacts towards an adaptive approach where monitoring data is used iteratively to refine hypotheses to better understand mechanisms. These methods will improve predictions of near-term ecological changes to better anticipate impacts and undesirable future states.

我的长期目标是评估，开发和应用分析方法来量化描述人口和生态系统动力学的比特，非生物和人为驱动因素之间的相互作用，尤其是在北部地区。鉴于当前的生态变化，迫切需要我们预测生态系统动力学的能力的改善。可靠的预测工具的开发是必不可少的，以更好地预期气候引起的对生态系统的影响。 My short-term research objectives seek to 1) improving sampling designs and methods for better integrating multiple data sources to model drivers of population and ecosystem dynamics, 2) foster new methods to combine indigenous and scientific knowledge to enhance our understanding of ecological changes in the North, and 3) developing models to produce near-term (seasonal/annual) iterative forests to better anticipate population and ecosystem changes and adapt management actions in real time.我的程序嵌入了近期迭代预测（NTIF）框架中。 NTIF使用模型集成了来自潜水员来源和临时量表（日，季节，年）的数据来描述生态系统响应。然后，随着新数据可用，它每天都会生产到年度规模的森林。在几项正在进行的长期研究的基础上，包括一些已经收集了有关州变量和驱动因素的大量数据的，我的小组将在实现有用的生态预测所需的所有步骤中提高知识。我们将评估研究设计如何结合新的替代方法（相机陷阱，来自头发DNA的个体识别，公民科学采样）可以降低估计值的不确定性并提高大型时空尺度上数据收集的效率。我们将开发面向过程的模型，结合多个数据源以解开哪些机制驱动生态变化。例如，我们将测试杂交或人为过程是否调节黑熊种群动态。我们将开发出强大的分析方法来模拟定性数据，从而使我们能够使用本地知识来检验以下假设：攀爬诱导的机制使主要北极物种之间的种群动态同步。我们将开发NTIF模型，并评估其预测未来生态系统状态的局限性。这些模型将检查气候对冬季壁虱（魁北克）和脱叶飞蛾（挪威）及其对驼鹿种群和桦木森林动态（死亡，再生）的后果的气候影响。我的计划将培训创新的HQP，该HQP将通过从当前对测量影响到适应性方法进行迭代数据的衡量影响的重点转移来加速科学学习，以改善假设以更好地了解机制。这些方法将改善对近期生态变化的预测，以更好地预期影响和不良的未来状态。