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

我的长期目标是评估、开发和应用分析方法来量化生态过程，描述人口和生态系统动态的生物、非生物和人为驱动因素之间的相互作用，特别是在北方地区。鉴于目前生态的剧烈变化，迫切需要提高我们预测生态系统动态的能力。开发可靠的预测工具对于更好地预测气候对生态系统的影响是必不可少的。我的短期研究目标是：1)改进抽样设计和方法，以便更好地整合多种数据源，以模拟人口和生态系统动态的驱动因素；2)促进新的方法，将本土和科学知识结合起来，以加强我们对北方生态变化的了解；3)开发模型，以产生近期(季节/年度)迭代预测，以更好地预测人口和生态系统的变化，并实时调整管理行动。我的程序嵌入了近期迭代预测(NTIF)框架中。NTIF使用综合来自不同来源和时间尺度(日、季、年)的数据的模型来描述生态系统响应。然后，它会产生每日到年度的规模预测，并在获得新数据时进行更新。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关于状态变量和驱动因素的广泛数据的研究，我的团队将在实现有用的生态预测所需的所有步骤上推进知识。我们将评估结合新的替代方法(相机陷阱、头发DNA的个人身份识别、公民科学采样)的研究设计如何减少估计的不确定性，并提高大时空尺度上的数据收集效率。我们将开发面向过程的模型，将多个数据源结合起来，揭示哪些机制驱动生态变化。例如，我们将测试气候或人为过程是否调节黑熊的种群动态。我们将开发强有力的分析方法来模拟定性数据，使我们能够使用本土知识来检验气候诱导机制使关键北极物种的种群动态同步的假设。我们将开发NTIF模型，并评估它们在预测未来生态系统状态方面的局限性。这些模型将研究气候对冬季扁虱(魁北克)和落叶蛾(挪威)向北传播的影响，以及它们对驼鹿种群和白桦林动态(死亡率、再生)的各自影响。我的计划将培训创新的HQP，他们将通过将当前的重点从测量影响转移到一种自适应方法来加速科学学习，在这种方法中，监测数据被反复使用来完善假设，以更好地理解机制。这些方法将改进对近期生态变化的预测，以更好地预测影响和不受欢迎的未来状态。

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.