Climate Change Data Portal
| CDI-Type I: Novel machine learning models for predicting species distributions in response to climate change | |
| 项目编号 | 0941748 |
| Weng-Keen Wong | |
| 项目主持机构 | Oregon State University |
| 开始日期 | 2009-10-01 |
| 结束日期 | 2013-09-30 |
| 英文摘要 | Earth's ecosystems are changing rapidly in response to human activities, threatening many species, but the precise effects of climate change, land cover change, and other factors on species distributions is a complex problem that has so far defied integrated analysis. Machine learning provides opportunities to address this ecological problem, and this ecological problem can stimulate advances in machine learning. In this proposal, a computer scientist, ecologist, and geoscientist join forces to develop a fundamentally different approach using computational methods to examine two major ecological problems: 1) Are species assemblages changing according to climate/land use change? and 2) Are species geographic ranges changing according to climate/land use change? Potentially complex relationships among land use, climate, and species inter-relationships may influence species distributions and changes in distributions. Analyses of species distribution and change are currently limited by the simplicity of the models used; most models only analyze the effect of environmental factors on the distribution of single species. The investigators propose to (1) create a new approach to species distribution modeling based on structured prediction and conditional topic models that will simultaneously discover and predict bird species assemblages (groups of co-occurring bird species); (2) develop novel contrast mining algorithms to discover geographic locations (?hotspots?) corresponding to significant changes in species assemblages (i.e., changes in topics) between time periods and (3) create innovative contrast mining algorithms to discover sites with significant changes in an individual species' spatial distribution and identify features which are most associated with these changes. This project's approach, employing structured prediction, conditional topic models, and contrast mining will allow testing a hitherto untestable ecological hypothesis: habitat connectivity facilitates species response to climate change, while simultaneously testing fundamental questions in ecology about the degree to which species respond individualistically or interdependently to climate and land use change. In terms of broader impacts, this proposal will contribute significant scientific knowledge to environmental policy about habitat restoration to mitigate climate change. The PIs will also provide outreach for their research to federal land management agencies through their involvement in a researchmanagement partnership between the US Forest Service and the NSF-funded H.J. Andrews Long-term Ecological Research program. |
| 学科分类 | 08 - 地球科学 |
| 资助机构 | US-NSF |
| 项目经费 | 609505 |
| 项目类型 | Standard Grant |
| 国家 | US |
| 语种 | 英语 |
| 文献类型 | 项目 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/71982 |
| 推荐引用方式 GB/T 7714 | Weng-Keen Wong.CDI-Type I: Novel machine learning models for predicting species distributions in response to climate change.2009. |
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