Statistically efficient integration of animal tracking data into ecological theory and evidence-based conservation

动物运动是一个关键的行为过程，它决定了个体、种群和物种如何与彼此和环境相互作用，是保护科学的核心组成部分。在跟踪技术进步的推动下，运动生态学已经从一个小众话题扩展到生态学中发展最快的领域之一。然而，问题是，用于分析这些新的和具有挑战性的数据集的统计技术落后了。这可能会限制追踪数据以可靠地告知物种保护的能力。我的长期目标是支持统计有效地将动物跟踪数据整合到生态理论和循证保护中。在接下来的5年里，我将通过应对三个关键挑战来实现这一目标。挑战(i)：准确估计从动物路径估计的潜在变量。我实验室的HQP将利用已建立的建模和仿真方法来开发路径衍生潜在变量的新估计器（例如，栖息地时间预算，过马路率等）。这些估算器的软件实现将免费提供给科学界。HQP将利用已有的跟踪数据集，应用这些工具来实现减轻人类与野生动物冲突的准确估计。挑战（ii）：遇到分布估计。现有的关于个体间相遇的建模工作主要集中在动物运动和相遇率之间的关系上，而没有研究将个体运动与环境中相遇事件的空间位置直接联系起来。HQP将通过引入新的理论分布，推导该分布，并在开放获取软件中实现其估计器来弥合这一差距。然后，这个估算器将与挑战(i)中的工具集成，以构建一个分析框架，以回答有关动物与车辆相互作用的问题。挑战（三）：宏观生态学和全球保护。我已经组装了一个广泛的经验动物跟踪数据集，包括来自76个全球分布物种的约1300个个体。通过将这些数据与强大的分析工具配对，我的学员和我将评估四个关键问题，旨在了解动物如何在全球范围内应对人为影响：a)运动速度的异速缩放；B)评估人为干扰对昼夜节律的影响；C)周期行为演变的评价；d)动物运动表型可塑性的进化制约因素的评价。作为该项目的一部分，将培训一组不同的HQP，包括至少3名博士，3名硕士和4名本科生。该实验室的研究和培训活动将帮助加拿大在快速发展的运动生态学领域处于领先地位。研究结果将直接适用于负责保护和恢复加拿大文化和生态重要物种的政府机构和非政府组织。

Animal movement is a key behavioural process that governs how individuals, populations, and species interact with each other and the environment, and is a core component of conservation science. Spurred by advances in tracking technologies, movement ecology has expanded from a niche topic to one of the fastest growing fields in ecology. Problematically, however, statistical techniques for analyzing these new and challenging datasets have lagged behind. This can limit the ability for tracking data to reliably inform species conservation. My long-term objective is to support the statistically efficient integration of animal tracking data into ecological theory and evidence-based conservation. Over the next 5 years, I will build toward this goal by tackling three key challenges. Challenge (i): Accurate estimation of latent variables estimated from animal paths. HQP in my lab will leverage established modelling and simulation methods to develop novel estimators of path-derived latent variables (e.g., habitat time budgets, road crossing rates, etc.). Software implementations of these estimators will be made freely available to the scientific community. Using pre-existing tracking datasets HQP will apply these tools to achieve accurate estimates for mitigating human-wildlife conflict. Challenge (ii): Encounter distribution estimation. Existing work on modelling inter-individual encounters has focused primarily on relating animal movement and encounter rates, while no research directly links individual movement with the spatial locations of encounter events in the environment. HQP will bridge this gap by introducing a new theoretical distribution, deriving this distribution, and implementing its estimator in open-access software. This estimator will then be integrated with the tools from challenge (i) to build an analytical framework for answering questions on animal-vehicle interactions. Challenge (iii): Macro-ecology and global conservation. I have assembled an extensive empirical animal tracking dataset, consisting of ~1300 individuals from 76 globally distributed species. By pairing these data with robust analytical tools, my trainees and I will evaluate four key questions aimed at understanding how animals are responding to anthropogenic impacts globally: a) the allometric scaling of movement speeds; b) an evaluation of the effects of anthropogenic disturbance on circadian rhythms; c) an evaluation of the evolution of periodic behaviour; and d) an evaluation of the evolutionary constraints on the phenotypic plasticity of animal movement. A diverse group of HQP, including at least 3 PhD, 3 MSc, and 4 UG will be trained as part of this project. The lab's research and training activities will help position Canada at the forefront of the rapidly growing field of movement ecology. Findings will have direct application for government agencies and NGOs charged with protecting and restoring Canada's culturally and ecologically important species.