IntBIO Collaborative Research: An integrative approach for projecting insect responses to a rapidly changing climate

在大陆尺度上预测物种对气候变化的反应是当前生态学研究的一个“重大挑战”。昆虫是气候和土地利用变化的敏感指标，最近的研究表明，在许多地理区域，昆虫数量普遍下降。为了预测整个范围内的变化，已经开发了各种物种分布模型，但很少考虑区域可变性、生态相互作用或物种适应不断变化的条件的潜力。该项目跨越了位于美国西南部、极地北部和东部温带地区的多个机构。将对五种分布广泛的蝴蝶物种进行一系列生理实验，这些蝴蝶物种的种群来自每个范围内的不同生物群。毛毛虫将受到一系列条件的影响，模拟过去、现在和未来的气候。它们的发育速度、存活率、免疫反应、遗传结构和基因表达(这些基因正在积极编码蛋白质)将被测量并用于建立预测分布变化的模型。社区(“公民”)科学家收集的数据将用于验证模型。该项目需要大量的跨学科协作，一个中心目标是在研究生和本科生层面招募不同的受训人员，并对他们进行“团队科学”方面的培训。项目学员将形成与项目范围一致并扩大的独立研究想法，并作为实验室间的交流在合作机构之间旅行和工作，以学习新技术并接触不同的研究理念。最后，该项目对昆虫生物多样性保护具有重要的管理意义。在大陆尺度上预测对气候变化的反应是当前生态学研究的一个“重大挑战”。昆虫是最多样和最重要的陆地动物分类群，受气候变化的影响很大。为了预测变化，物种分布模型(SDMS)在许多分类群中得到了广泛的应用。然而，SDMS很少考虑生态相互作用、可塑性或进化适应潜力，因为需要大量的生理和生态数据来对这些模型进行参数化。鳞翅目的生物学，特别是蝴蝶的生物学被观察得非常好，因此，在过去知识的基础上建立和收集额外的数据是可行的，使机制能够更无缝地整合到SDMS中。每个物种的多个种群将来自其范围内的不同生物群。毛毛虫将在普通花园中饲养，在一系列温度条件下模拟过去、现在和未来的气候。它们的发育速度、存活率、免疫反应、遗传结构和基因表达将被测量并用于建立预测未来分布的模型。社区(“公民”)科学家收集的分布数据将用于验证和改进模型，并允许对不确定性进行可靠的估计。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Projecting species’ responses to climate change at continental scales is a current “grand challenge” of ecological research. Insects are sensitive indicators of both climate and land-use change and recent studies indicate widespread declines in many geographic regions. To predict changes across entire ranges, a variety of species distribution models have been developed, but rarely account for regional variability, ecological interactions or a species’ potential to adapt to changing conditions. This project spans multiple institutions situated in the United States’ southwest, polar north, and temperate eastern regions. A series of physiological experiments will be implemented for five widespread butterfly species with populations sourced from different biomes within each of their ranges. Caterpillars will be subjected to a range of conditions mimicking past, current and future climates. Their development rate, survivorship, immune response, and genetic structure and gene expression (which genes are actively coding for proteins) will be measured and used to build models that predict distributional shifts. Data collected by community (“citizen”) scientists will be used to validate the models. This project requires substantial cross-disciplinary collaboration, and a central goal is to recruit diverse trainees at the graduate and undergraduate levels and train them in the “science of team science”. Project trainees will develop independent research ideas that align with and expand the project’s scope and travel between and work at collaborating institutions as an inter-lab exchange to learn new techniques and be exposed to different research philosophies. Finally, the project has significant management implications for insect biodiversity conservation. Projecting responses to climate change at continent scales is a current “grand challenge” of ecological research. Insects are the most diverse and ecologically important terrestrial animal taxon and are strongly affected by climate change. To predict changes, species distribution models (SDMs) have been widely implemented across many taxa. SDMs, however, rarely account for ecological interactions, plasticity or evolutionary adaptive potential owing to the extensive physiological and ecological data required to parameterize such models. The biology of Lepidoptera, particularly butterflies, is extremely well observed, thus it is logistically feasible to build upon past knowledge and collect additional data that enables mechanisms to be more seamlessly integrated into SDMs. Multiple populations for each species will be sourced from different biomes across its range. Caterpillars will be reared in common gardens under a range of temperature conditions mimicking past, current and future climates. Their development rate, survivorship, immune response, genetic structure and gene expression will be measured and used to build models that predict future distributions. Distribution data collected by community (“citizen”) scientists will be used to validate and improve models and allow robust estimates of uncertainty.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.