Seed Dispersal Workshop Proposal; Annapolis, MD - Spring, 2016

扩散是种群传播、从地方到全球尺度的生物多样性模式、新环境中的基因流动和潜在适应以及物种对全球变化的反应的关键过程。全球变化过程，如气候变化和破碎化，改变了物种在当地的栖息地条件，也改变了物种扩散的生态和进化。这些变化影响了物种移动或适应这些过程的能力。本次研讨会将聚集不同的生态学家和数学生物学家，他们研究不同尺度、方法和系统的扩散，并将汇集现有经验信息、理论概念和数学方法的知识。研讨会的主要成果将是评估如何将数据与理论预测和新的分析、计算和统计进展相结合，以研究扩散。其结果将有助于提高对全球变化作出反应的能力。科学家之间的合作也将得到促进。研讨会将确定目前我们对种子传播在植物种群中的作用的理解差距，并确定如何解决这些突出的差距，以便在全球变化下对植物种群进行预测性理解。种子传播生态学在很大程度上是基于对少数物种的短期、局地尺度的经验研究，或者基于经常做出简化假设的理论传播模型。这些因素限制了概括性，限制了定量预测的能力。通过将数据与模型相结合，研讨会将引导计算机实验，以a)获得对扩散在植物种群动态中的作用的机制理解；B)用经验数据检验理论预测；c)进行敏感性分析，确定结论对可用数据类型、缺失数据类型和不同类型模型的稳健性。知识差距和阻碍进步的障碍将被发现，科学网络将得到加强。

Dispersal is a key process in the spread of populations, in biodiversity patterns from local to global scales, in gene flow and potential adaptation in novel environments, and in species responses to global change. Global change processes, such as climate change and fragmentation, alter local habitat conditions of species, and also the ecology and evolution of dispersal. These changes affect the ability of species to move or adapt in response to these processes. This workshop will assemble a diverse group of ecologists and mathematical biologists who study dispersal across scales, methodologies, and systems and who will bring together knowledge of existing empirical information, theoretical concepts, and mathematical approaches. The primary outcome of the workshop will be an evaluation of how data can be integrated with theoretical predictions and novel analytical, computational, and statistical advances for studying dispersal. The outcome will contribute to the ability to respond to global change. Collaborations among scientists will also be fostered.A workshop will identify current gaps in our understanding of the role of seed dispersal in plant populations and determine how to address these outstanding gaps in order to move towards a predictive understanding of plant populations under global change. Seed dispersal ecology is largely based on short-term, local-scale empirical studies for a small number of species or on theoretical dispersal models that often make simplified assumptions. These factors limit generality the ability to make quantitative predictions. By integrating data with models, the workshop will lead to computer experiments to a) gain a mechanistic understanding of the role of dispersal in plant population dynamics; b) test theoretical predictions using empirical data; and c) conduct sensitivity analyses to determine the robustness of conclusions to the type of available data, to missing data, and to different types of models. Gaps in knowledge and obstacles to progress will be identified, and scientific networking will be enhanced.