Collaborative proposal: Combining NEON and remotely sensed habitats to determine climate impacts on community dynamics

合作提案：结合 NEON 和遥感栖息地来确定气候对群落动态的影响

• 批准号: 1754656
• 负责人: Roland Kays
• 金额: $ 24万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1754656&HistoricalAwards=false
• 关键词: Collaborative; proposal; Combining; NEON; remotely

The impact of environmental change on biological communities will depend on interactions involving the local habitat and species that interact with one another - as each species responds directly to environmental conditions, it indirectly affects all of the species with which it interacts. Species interactions complicate our ability to predict organismal response to environmental change, because each species experiences habitat complexity at a different scale - from flightless insects to large vertebrates. Current species distribution models used to anticipate community responses to climate predict species in isolation from one another, ignoring interactions. This research approach will advance ecological forecasting methods by a developing a new modeling approach that analyzes the combined responses of species to climate, habitat, and one another. The study offers a framework for synthesizing environmental and biological data across the National Ecological Observation Network (NEON) network, one that can be exploited by the scientific community for integration with their own analyses. The project will engage the public through citizen-assisted identification of animal images. New data on large mammals and seed production from NEON sites will be made available to the community, and forecasts of community responses to climate will be made available through a web site. A workshop will be offered for training of advanced doctoral students, postdocs, and academic and agency scientists in the use of the modeling tools and NEON data. The study will train two graduate students, including a program in science communication, and involve four undergraduate students in fieldwork. This research will synthesize taxonomically diverse communities that are monitored in NEON (ground beetles, vascular plants, small mammals, birds) with new data on mast, and large mammal surveys. Mast is one of several habitat variables that interacts with climate variation. A focus on the mast system of pulsed seed and fruit production from trees includes vertebrate consumers, as well as indirect interactions with arthropod competitors and vertebrate predators. Remotely-sensed imagery and the NEON airborne observatory will be used to characterize habitat diversity. New sampling at NEON sites for large mammals and seed production will add key components that allow us to evaluate the community dynamics of masting tree systems at broad scales. Results of this analysis will be used to forecast community change and reorganization, including prediction and attribution of climate risk by species and habitat and how it is shared across species groups.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.

环境变化对生物群落的影响将取决于当地栖息地和彼此相互作用的物种的相互作用——由于每个物种直接对环境条件做出反应，它间接影响与其相互作用的所有物种。物种相互作用使我们预测生物体对环境变化的反应的能力变得复杂，因为每个物种都经历着不同规模的栖息地复杂性——从不会飞的昆虫到大型脊椎动物。当前用于预测群落对气候反应的物种分布模型预测物种是彼此孤立的，忽略了相互作用。这一研究方法将通过开发一种新的建模方法来推进生态预测方法，该方法可以分析物种对气候、栖息地和彼此之间的综合反应。该研究提供了一个综合国家生态观测网络（NEON）网络中的环境和生物数据的框架，科学界可以利用该框架与他们自己的分析相结合。该项目将通过公民协助识别动物图像来吸引公众。有关大型哺乳动物和 NEON 站点种子生产的新数据将向社区提供，并且将通过网站提供社区对气候反应的预测。将举办一个研讨会，培训高级博士生、博士后以及学术和机构科学家如何使用建模工具和 NEON 数据。该研究将培训两名研究生，其中包括一个科学传播项目，并让四名本科生参与实地考察。这项研究将综合 NEON 中监测的分类多样化群落（地面甲虫、维管植物、小型哺乳动物、鸟类）以及桅杆和大型哺乳动物调查的新数据。桅杆是与气候变化相互作用的几个栖息地变量之一。对树木脉冲种子和果实生产的桅杆系统的关注包括脊椎动物消费者，以及与节肢动物竞争者和脊椎动物捕食者的间接相互作用。遥感图像和 NEON 机载观测站将用于描述栖息地多样性。 NEON 大型哺乳动物和种子生产地点的新采样将增加关键组成部分，使我们能够在大范围内评估肥大树系统的群落动态。该分析的结果将用于预测群落变化和重组，包括按物种和栖息地预测和归因气候风险以及如何在物种群体之间分担。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States

• DOI: 10.1002/ecy.3353
• 发表时间: 2021-06-01
• 期刊: ECOLOGY
• 影响因子: 4.8
• 作者: Cove, Michael V.;Kays, Roland;McShea, Andwilliam J.
• 通讯作者: McShea, Andwilliam J.

Is there tree senescence? The fecundity evidence

• DOI: 10.1073/pnas.2106130118
• 发表时间: 2021-08
• 期刊: Proceedings of the National Academy of Sciences
• 作者: T. Qiu;Marie-Claire Aravena;R. Andrus;D. Ascoli;Y. Bergeron;R. Berretti;M. Bogdziewicz;T. Boivin;R. Bonal;Thomas Caignard;R. Calama;J. Julio Camarero;C. Clark;B. Courbaud;S. Delzon;Sergio Donoso Calderon;W. Farfán-Ríos;C. Gehring;G. Gilbert;C. Greenberg;Q. Guo;Janneke Hille Ris Lambers;Kazuhiko Hoshizaki;I. Ibáñez;V. Journé;Christopher L. Kilner;R. Kobe;W. Koenig;G. Kunstler;J. M. LaMontagne;Mateusz Ledwoń;J. Lutz;R. Motta;J. Myers;T. Nagel;C. Nuñez;I. Pearse;Łukasz Piechnik;J. Poulsen;Renata Poulton-Kamakura;M. Redmond;Chantal D. Reid;K. Rodman;C. Scher;Harald Schmidt Van Marle;Barbara Seget;Shubhi Sharma;M. Silman;J. Swenson;M. Swift;M. Uriarte;G. Vacchiano;T. Veblen;A. Whipple;T. Whitham;A. Wion;S. Wright;K. Zhu;J. Zimmerman;M. Żywiec;J. Clark

Which mammals can be identified from camera traps and crowdsourced photographs?

哪些哺乳动物可以从相机陷阱和众包照片中识别出来？

• DOI: 10.1093/jmammal/gyac021
• 发表时间: 2022
• 期刊: Journal of Mammalogy
• 影响因子: 1.7
• 作者: Kays, Roland;Lasky, Monica;Allen, Maximilian L;Dowler, Robert C;Hawkins, Melissa T;Hope, Andrew G;Kohli, Brooks A;Mathis, Verity L;McLean, Bryan;Olson, Link E
• 通讯作者: Olson, Link E

The Internet of Animals: what it is, what it could be

• DOI: 10.1016/j.tree.2023.04.007
• 发表时间: 2023-08-08
• 期刊: TRENDS IN ECOLOGY & EVOLUTION
• 影响因子: 16.8
• 作者: Kays, Roland;Wikelski, Martin
• 通讯作者: Wikelski, Martin

The emergent interactions that govern biodiversity change

• DOI: 10.1073/pnas.2003852117
• 发表时间: 2020-07-21
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Clark, James S.;Scher, C. Lane;Swift, Margaret
• 通讯作者: Swift, Margaret