Linking demographic theory and data to forecast the dynamics of spatially-structured seasonally-mobile populations

野生种群正面临前所未有的威胁。许多栖息地是自然斑驳的或被人类活动碎片化的。许多生境在适宜性方面也表现出季节性变化，气候模型预测极端季节性天气事件(例如风暴、干旱、洪水)的频率增加，可能导致高死亡率或阻止受影响地区的繁殖。因此，生态学的关键目标是了解和预测居住在空间结构季节性环境中的种群的动态和持久性，包括种群对改变人口结构的极端季节性事件的反应。时空种群动态最终取决于四个主要人口速率：生存、繁殖、扩散(繁殖区之间的永久迁移)和迁徙(可逆的季节性迁移)。然而，到目前为止，还没有任何实证研究或人口动力学理论或模型完全量化或考虑迁徙的时空变化以及生存、扩散和繁殖的变化。因此，我们缺乏数据、理论和模型来量化所有四个关键人口率变化对人口动态的影响，这严重限制了我们预测空间结构的季节性流动人口动态的能力。现在很明显，部分迁移在性质上是非常常见的，其中人口包括居民和移民个人的混合。部分迁徙的人口可能表现出复杂的人口学和动态，因为迁徙的结构性变化可能导致当前或随后的生存、分散或繁殖的结构性变化，而极端事件可能导致突发性迁徙。人口动态模型和实证研究忽略了移徙中的时空变化以及由此产生的遗留效应，因此忽视了人口变化的基本轴线。我们将提供对空间结构的季节性流动人口的人口学和动力学的新的经验和理论理解，以及人口动态预测的新能力，方法是：1)提供推动人口动态的关键人口速率和关系的首次经验估计，包括对极端事件的人口反应。我们将使用我们收集的关于空间结构的部分迁徙的欧洲沙格人口的一个杰出的大规模多年数据集。在2009-2017年间，我们在6个繁育群体中标记了14,500只个体，收集了44,200次冬季目击记录，以识别居民和候鸟，并测量了个体的扩散和繁殖。2012-2014年，该系统经历了极端的冬季风暴，导致高死亡率。这个系统现在提供了一个无与伦比的机会来量化个体迁徙中特定性别、年龄和亚种群的变化，以及在自然极端季节性事件之前、期间和之后与生存、扩散和繁殖相关的协变。2)提供了新的理论来确定人口对季节性人口变化的动态反应的一般原理。我们将建立和分析基于个体的模拟模型，量化涉及迁徙的人口协变的人口后果。在给定不同的生活史和空间栖息地结构的情况下，我们将量化人口增长率对假设形式的结构化部分迁移和与生存、扩散和繁殖的协变性的敏感性，以及对极端事件和由此产生的人口扰动的假设制度的敏感性。3)为预测时空人口动态提供新的灵活的建模框架。我们将在我们最先进的RangeShifter软件中构建新的迁移模型，用于空间显式的人口动态建模，并模拟示范欧洲SHIG系统的时空动力学，从而展示新的灵活的预测能力和保护政策。

Wild populations are facing unprecedented threats. Many habitats are naturally patchy or being fragmented by human activities. Many habitats also show seasonal variation in suitability, and climate models predict increasing frequencies of extreme seasonal weather events (e.g. storms, droughts, floods) that could cause high mortality or prevent reproduction in affected areas. Critical aims in ecology are therefore to understand and forecast the dynamics and persistence of populations inhabiting spatially-structured seasonal environments, including population responses to extreme seasonal events that alter demography.Spatio-temporal population dynamics ultimately depend on four primary demographic rates: survival, reproduction, dispersal (permanent movements among breeding areas) and migration (reversible seasonal movements). However, to date, no empirical studies or population dynamic theory or models have fully quantified or considered spatio-temporal variation in migration alongside variation in survival, dispersal and reproduction. We consequently lack data, theory and models that quantify population dynamic effects of variation in and among all four key demographic rates, severely limiting our ability to forecast the dynamics of spatially-structured seasonally-mobile populations.It is now clear that partial migration, where populations comprise mixtures of resident and migrant individuals, is very common in nature. Partially-migratory populations could show complex demography and dynamics because structured variation in migration could cause structured variation in current or subsequent survival, dispersal or reproduction, while extreme events could cause irruptive migration. Population dynamic models and empirical studies that ignore spatio-temporal variation in migration, and resulting carry-over effects, therefore ignore fundamental axes of demographic variation. We will provide new empirical and theoretical understanding of the demography and dynamics of spatially-structured seasonally-mobile populations, and new capability for population dynamic forecasting, by:1) Providing first empirical estimates of key demographic rates and relationships that drive population dynamics, including demographic responses to extreme events.We will use an outstanding large-scale multi-year dataset that we have collected on a spatially-structured partially-migratory European shag population. During 2009-2017 we marked 14,500 individuals across 6 breeding colonies, collected 44,200 winter sightings to identify residents and migrants, and measured individual dispersal and reproduction. In 2012-2014, the system experienced extreme winter storms, causing high mortality. This system now provides an unrivalled opportunity to quantify sex-, age- and sub-population-specific variation in individual migration, and covariation with survival, dispersal and reproduction, before, during and after natural extreme seasonal events.2) Providing new theory that identifies general principles of population dynamic responses to seasonal demographic variation.We will build and analyse individual-based simulation models that quantify population consequences of demographic covariation involving migration. We will quantify sensitivities of population growth rate to hypothesised forms of structured partial migration and covariation with survival, dispersal and reproduction given varying life-histories and spatial habitat structures, and to hypothesised regimes of extreme events and resulting demographic perturbations.3) Providing a new flexible modelling framework for forecasting spatio-temporal population dynamics.We will build new migration models into our state-of-the-art 'RangeShifter' software for spatially-explicit population dynamic modelling and simulate spatio-temporal dynamics of the exemplar European shag system, thereby demonstrating new flexible forecasting capability and informing conservation policy.