Beta diversity patterns and their origins

准确预测人类造成或加剧的扰动——包括气候变化、栖息地丧失和外来物种入侵——将如何影响群落和生态系统的结构和功能，对于制定有效的适应性管理策略至关重要。实现准确预测的关键是对控制生态群落分类、功能和系统发育组成变化的因素有充分的了解。特别是，需要更好的资料，了解促进当地组装过程的重要性超过与区域物种库有关的突发事件的条件。例如，如果局部群落只是区域物种库的随机子集，那么就不需要调用局部过程来解释群落之间观察到的组成变化。通过使用最近设计的零模型分析群落组成的变化（称为“beta多样性”），可以深入了解这个问题。这些零模型有助于区分那些与本地确定性驱动因素一致的模式，与那些与区域物种库的随机组合一致的模式。到目前为止，应用这些方法的少数几项研究取得了重要的和意想不到的发现，例如，包括观察到，一旦考虑到区域（伽马）多样性的差异，热带森林在分类组成方面并不比温带森林表现出更多的变化。这一发现表明，在全球各种研究系统中记录的β多样性，可能并不像传统观点那样，是由于当地组装过程的差异而产生的。相反，它可能最终起源于影响区域物种库特征的历史/生物地理过程。我建议将这些零模型方法广泛应用于各种关键特征不同的研究系统，包括分类群的扩散能力（例如陆生植物与淡水硅藻）和生态系统类型（例如陆生植物与水生植物）。具体的研究系统包括不列颠哥伦比亚省森林中的维管植物、北美淡水湖中的硅藻、美国河流中的大型无脊椎动物和美国湖泊中的浮游动物。然后，我建议探讨随机集合和区域池偶然性对古湖泊学的影响，这是一个在经典β多样性理论中具有强大而持久根源的应用领域。古湖泊学家利用淡水硅藻（和其他生物）中β多样性的时空模式来推断环境变化。虽然非常成功，但古湖泊学方法是建立在关于群落聚集的过时观念之上的。我的第二个目标是对古湖泊传递函数的精度如何根据局部决定论与随机组合和区域池偶然性的不同假设而变化进行第一次严格评估。这项工作的最终目标是改进目前的定量方法，这些方法在很大程度上忽略了随机和确定性组装沿环境梯度的重要性的系统变化的潜力。

Accurately predicting how perturbations caused or exacerbated by humans - including climate change, habitat loss, and exotic species invasions - will affect the structure and functioning of communities and ecosystems is essential to devising effective adaptive management strategies. Key to achieving accurate predictions is an adequate understanding of the factors that govern variation in the taxonomic, functional, and phylogenetic composition of ecological communities. In particular, better information is required about the conditions that promote the importance of local assembly processes over contingencies related to the regional species pool. For example, if local communities are simply random subsets of the regional species pool, then local processes need not be invoked to explain variation in composition observed among communities. Insights into this issue can be gained by analyzing variation in community composition (called "beta diversity") using recently devised null models. These null models help to distinguish patterns that are consistent with a predominant role for local, deterministic drivers of community structure, from those consistent with stochastic assembly from the regional species pool. The few studies that have applied these methods thus far have yielded important and unanticipated discoveries, including, for example, the observation that tropical forests exhibit no more variation in taxonomic composition than temperate forests once differences in regional (gamma) diversity are accounted for. This finding suggests that much of the beta diversity that has been documented across the globe in a variety of study systems may not, as traditional thought, arise due to variation in local assembly processes. Instead, it might ultimately originate in historical / biogeographical processes that influence the characteristics of regional species pools. I propose to apply these null model methods broadly, across a variety of study systems that vary in key characteristics, including dispersal capacities of the taxa (e.g. terrestrial plants versus freshwater diatoms) and ecosystem type (e.g. terrestrial versus aquatic). The specific study systems include vascular plants of British Columbia forests, diatoms of the freshwater lakes of North America, macroinvertebrates in U.S. rivers, and zooplankton in U.S. lakes. I then propose to explore the implications that stochastic assembly and regional pool contingencies have for paleolimnology, an applied field with strong and persistent roots in classic beta diversity theory. Paleolimnologists use spatial and temporal patterns of beta diversity among freshwater diatoms (and other organisms) to infer environmental change. Although highly successful, the paleolimnological method is founded on outdated ideas about community assembly. My second objective is to conduct the first rigorous assessment of how the precision of paleolimnological transfer functions vary according to different assumptions about the roles of local determinism versus stochastic assembly and regional pool contingencies. The ultimate goal of this work is to improve upon current quantitative methods that largely ignore the potential for systematic variation in the importance of stochastic and deterministic assembly along environmental gradients.