Collaborative QEIB Research: Spatio-Temporal Models of Species Distributions and Biodiversity at High Resolution - Integrating Climate and Population Responses

QEIB 合作研究：高分辨率物种分布和生物多样性时空模型 - 整合气候和人口响应

• 批准号: 0516320
• 负责人: John Silander
• 金额: $ 45万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0516320&HistoricalAwards=false
• 关键词: Collaborative; QEIB; Research; Spatio; Temporal

Understanding the distribution of species and patterns of biodiversity across the earth is a central problem in ecology and biogeography. Key to this problem is understanding how present, past and future climate may drive biological patterns spatially and temporally. This project will focus on developing Bayesian hierarchical, statistical models that integrate pattern and process across many levels, from populations of organisms to species to regional climates. The world is in an extraordinary phase of climate change that will drive potentially dramatic changes in species distribution patterns. Many scientists have pointed to a looming conservation crisis. Southern Africa provides a case study where this is especially true. Yet the tools currently at hand are inadequate for providing sufficient predictive power (and uncertainties thereof) with sufficient spatial resolution to understand, plan for and address the crisis. This study will concentrate on predicting and explaining plant species distributions (especially for the Proteaceae family) in the Cape Floristic Region (CFR) of South Africa, using data on plant distributions and performances to inform the downscaling of climate at a regional level. It will use the climate data to learn how species respond to changing climate. The CFR is a region with exceptionally high diversity and levels of geographic differentiation and with detailed climate and species distribution records. Novel statistical models will be developed to predict and explain species abundances and population-level performances, and refine forecasts for recent past and near future climates. The insight gained on the processes specific to this system, as well as the models and tools we develop, will be broadly applicable to understanding other systems elsewhere in the world. This project provides a unique opportunity for trans-disciplinary training of students and post-doctoral associates in ecology, climatology, statistics, and modeling. It will developed an active recruiting and retention program for underserved students, and will cultivate a K-12 educational outreach program, with US-South African linkages, that focuses on issues of biodiversity and climate change.

了解地球上物种的分布和生物多样性的模式是生态学和生物地理学的中心问题。这个问题的关键是了解当前、过去和未来的气候如何在空间和时间上驱动生物模式。该项目将重点开发贝叶斯分层统计模型，该模型整合了从生物种群到物种再到区域气候等多个层面的模式和过程。世界正处于气候变化的非同寻常阶段，这将导致物种分布模式发生潜在的巨大变化。许多科学家指出，保护危机迫在眉睫。南部非洲提供了一个尤其如此的案例研究。然而，目前现有的工具不足以提供足够的预测能力（及其不确定性）和足够的空间分辨率来理解、规划和解决危机。 这项研究将集中于预测和解释南非开普植物区 (CFR) 的植物物种分布（特别是山龙眼科），利用植物分布和表现的数据为区域层面的气候降尺度提供信息。它将利用气候数据来了解物种如何应对气候变化。 CFR 是一个具有极高多样性和地理分化程度的地区，并且有详细的气候和物种分布记录。 将开发新的统计模型来预测和解释物种丰度和种群水平的表现，并完善对最近和不久的将来气候的预测。对该系统特定流程以及我们开发的模型和工具的深入了解将广泛适用于理解世界其他地方的其他系统。 该项目为学生和博士后在生态学、气候学、统计学和建模方面的跨学科培训提供了独特的机会。 它将为服务不足的学生制定一项积极的招募和保留计划，并将与美国和南非建立联系，制定一个 K-12 教育外展计划，重点关注生物多样性和气候变化问题。