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

扩散是种群扩散、从局部到全球的生物多样性格局、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，如气候变化和碎片化，改变了当地物种的栖息地条件，也改变了物种扩散的生态和进化。这些变化会影响物种对这些过程做出反应的移动或适应能力。这次研讨会将汇集不同的生态学家和数学生物学家，他们研究跨尺度、方法论和系统的扩散，并将现有经验信息、理论概念和数学方法的知识聚集在一起。研讨会的主要成果将是对如何将数据与理论预测以及用于研究扩散的新的分析、计算和统计进展相结合的评估。这一结果将有助于提高应对全球变化的能力。还将促进科学家之间的合作。一个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方面的差距，并确定如何解决这些突出差距，以便走向对全球变化下植物种群的预测性理解。种子传播生态学在很大程度上是基于对少数物种的短期、局部规模的经验研究，或者基于经常做出简化假设的理论传播模型。这些因素限制了做出定量预测的能力。通过将数据与模型相结合，研讨会将导致进行计算机实验，以a)从机械上了解扩散在植物种群动态中的作用；b)使用经验数据检验理论预测；以及c)进行敏感性分析，以确定结论对现有数据类型、缺失数据和不同类型模型的稳健性。将查明知识方面的差距和取得进展的障碍，并加强科学网络。

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.