Collaborative Research: Processes Determining the Abundance of Terrestrial Wildlife Communities Across Large Scales
合作研究:大规模确定陆地野生动物群落丰度的过程
基本信息
- 批准号:1232442
- 负责人:
- 金额:$ 83.69万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2011
- 资助国家:美国
- 起止时间:2011-12-22 至 2016-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Understanding the processes determining abundance of terrestrial wildlife communities across large scales and estimating the abundance of wildlife across large areas remain a major challenge. For most species, the factors that regulate their distribution and yearly fluctuations in population size are unknown. This project takes advantage of the key innovation of using motion sensitive camera traps as a network of sensors for estimating animal abundance. This new approach can produce abundance data for any terrestrial animal greater than 100g, typically about 60% of the terrestrial animals in the eastern USA. Furthermore, the method is amenable to citizen science programs, without the biases or data quality issues typical of other programs, opening the possibility of a sustainable dense sampling effort across large areas. Software will be developed efficiently enter and to manage camera images and associated data. Using standardize field techniques citizens groups will sample their local wildlife communities with cameras. The resulting data will be analyzed using new multi-scale statistical models to discover the processes regulating wildlife abundance over large areas. Mapping the local abundance of wildlife populations across broad areas will be key to understanding the mechanisms responsible for changes resulting from land-management decisions and regional climate variation. By involving citizens in data collection this project will be helping local wildlife populations. All data and images will be made freely available online, providing a tool not only for scientists, but also to give the public a new window into the animal communities of their region.
了解大规模确定陆地野生动物群落丰度的过程和估计大面积野生动物丰度仍然是一个重大挑战。 对大多数物种来说,调节其分布和种群规模年度波动的因素尚不清楚。 该项目利用了使用运动敏感相机陷阱作为估计动物丰度的传感器网络的关键创新。这种新方法可以产生任何大于100克的陆生动物的丰度数据,通常约占美国东部陆生动物的60%。此外,该方法适用于公民科学计划,没有其他计划典型的偏见或数据质量问题,为大面积可持续密集采样工作提供了可能性。软件将被开发有效地输入和管理相机图像和相关数据。使用标准化的野外技术,公民团体将用相机对当地的野生动物群落进行采样。将使用新的多尺度统计模型分析由此产生的数据,以发现调节大面积野生动物丰度的过程。绘制广泛地区野生动物种群的本地丰度图将是了解土地管理决策和区域气候变化导致变化的机制的关键。通过让公民参与数据收集,该项目将帮助当地野生动物种群。所有数据和图像都将在网上免费提供,不仅为科学家提供了一个工具,也为公众提供了一个了解其所在地区动物群落的新窗口。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Roland Kays其他文献
Optimal timing to estimate moose Alces alces demographic parameters using remote cameras
使用远程摄像机估算驼鹿阿尔克阿尔克人口统计参数的最佳时间
- DOI:
10.1038/s41598-025-05603-y - 发表时间:
2025-07-01 - 期刊:
- 影响因子:3.900
- 作者:
Hailey Boone;Mark Romanski;Kenneth Kellner;Roland Kays;Lynette Potvin;Gary Roloff;Jerrold Belant - 通讯作者:
Jerrold Belant
Can mammals thrive near urban areas in the Neotropics? Characterizing the community of a reclaimed tropical forest
- DOI:
10.1007/s42965-020-00134-1 - 发表时间:
2021-01-19 - 期刊:
- 影响因子:1.700
- 作者:
Stephanie Schuttler;Serano Ramcharan;Hailey Boone;Spencer Stone;Brian J. O’Shea;Krisna Gajapersad;Roland Kays - 通讯作者:
Roland Kays
Mammals in and around suburban yards, and the attraction of chicken coops
- DOI:
10.1007/s11252-014-0347-2 - 发表时间:
2014-01-26 - 期刊:
- 影响因子:2.400
- 作者:
Roland Kays;Arielle Waldstein Parsons - 通讯作者:
Arielle Waldstein Parsons
Clarifying assumptions behind the estimation of animal density from camera trap rates
澄清根据相机陷阱率估算动物密度背后的假设
- DOI:
- 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
J. M. Rowcliffe;Roland Kays;Roland Kays;C. Carbone;Patrick A. Jansen;Patrick A. Jansen - 通讯作者:
Patrick A. Jansen
The Internet of Animals: what it is, what it could be
动物互联网:它是什么,它可能是什么
- DOI:
10.1016/j.tree.2023.04.007 - 发表时间:
2023-09-01 - 期刊:
- 影响因子:17.300
- 作者:
Roland Kays;Martin Wikelski - 通讯作者:
Martin Wikelski
Roland Kays的其他文献
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{{ truncateString('Roland Kays', 18)}}的其他基金
Collaborative Research: Integrated distribution models for North American mammals as tests of niche conservatism.
合作研究:北美哺乳动物的综合分布模型作为生态位保守主义的测试。
- 批准号:
2206783 - 财政年份:2022
- 资助金额:
$ 83.69万 - 项目类别:
Standard Grant
Collaborative Research: Continent-wide forest recruitment change: the interactions between climate, habitat, and consumers
合作研究:全大陆森林补充变化:气候、栖息地和消费者之间的相互作用
- 批准号:
2211768 - 财政年份:2022
- 资助金额:
$ 83.69万 - 项目类别:
Standard Grant
Collaborative Research: IIBR Informatics: Data integration to improve population distribution estimation with animal tracking data
合作研究:IIBR 信息学:数据集成,利用动物追踪数据改进人口分布估计
- 批准号:
1914928 - 财政年份:2019
- 资助金额:
$ 83.69万 - 项目类别:
Standard Grant
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 - 财政年份:2018
- 资助金额:
$ 83.69万 - 项目类别:
Standard Grant
Collaborative proposal: ABI Sustaining: The Environmental-Data Automated Track Annotation (Env-DATA) system
合作提案:ABI Sustaining:环境数据自动轨迹注释(Env-DATA)系统
- 批准号:
1564382 - 财政年份:2016
- 资助金额:
$ 83.69万 - 项目类别:
Standard Grant
Collaborative Research EAGER-NEON: Probabilistic Forecasting of Biodiversity Response to Intensifying Drought by Combining NEON, National Climate, Species, and Trait Data Bases
合作研究 EAGER-NEON:结合 NEON、国家气候、物种和性状数据库,对生物多样性对加剧干旱的反应进行概率预测
- 批准号:
1550907 - 财政年份:2015
- 资助金额:
$ 83.69万 - 项目类别:
Standard Grant
CyberSEES: Type 2: Collaborative Research: Cyber-infrastructure and Technologies to Support Large-Scale Wildlife Monitoring and Research for Wildlife and Ecology Sustainability
CyberSEES:类型 2:协作研究:支持大规模野生动物监测以及野生动物和生态可持续性研究的网络基础设施和技术
- 批准号:
1539622 - 财政年份:2015
- 资助金额:
$ 83.69万 - 项目类别:
Standard Grant
Collaborative Research: Processes Determining the Abundance of Terrestrial Wildlife Communities Across Large Scales
合作研究:大规模确定陆地野生动物群落丰度的过程
- 批准号:
1065822 - 财政年份:2011
- 资助金额:
$ 83.69万 - 项目类别:
Standard Grant
DEB (Ecology): Seed Dispersal by Central American Agoutis - A Mutualism Conditioned by Predators or Food?
DEB(生态学):中美洲刺豚鼠的种子传播 - 由捕食者或食物调节的互利共生?
- 批准号:
0717071 - 财政年份:2007
- 资助金额:
$ 83.69万 - 项目类别:
Standard Grant
BD&I: MoveBank: Integrated database for networked organism tracking.
BD
- 批准号:
0756920 - 财政年份:2007
- 资助金额:
$ 83.69万 - 项目类别:
Standard Grant
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