Sparse modeling for climate variable selection across trophic levels.
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| Title: | Sparse modeling for climate variable selection across trophic levels. |
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| Authors: | Grames, Eliza M.1,2 egrames@binghamton.edu, Forister, Matthew L.1 |
| Source: | Ecology. Mar2024, Vol. 105 Issue 3, p1-10. 10p. |
| Subjects: | Food chains, Atmospheric models, Conservation biology, Plant populations, Biological fitness, Bird populations, Plant phenology |
| Abstract: | Understanding how populations respond to climate is fundamentally important to many questions in ecology, evolution, and conservation biology. Climate is complex and multifaceted, with aspects affecting populations in different and sometimes unexpected ways. Thus, when measuring the changing climate it is important to consider the complexity of the phenomenon and the number of ways it can be characterized through different metrics. We used a Bayesian sparse modeling approach to select among 80 metrics of climate and applied the approach to 19 datasets of bird, insect, and plant population responses to abiotic conditions as case studies of how the method can be applied for climate variable selection in a time series context. For phenological datasets, mean spring temperature was frequently selected as an important climate driver, while selected predictors were more diverse for population metrics such as abundance or reproductive success. The climate variable selection approach presented here can help to identify potential climate metrics when there is limited physiological or mechanistic information to make an a priori variable selection, and is broadly applicable across studies on population responses to climate. [ABSTRACT FROM AUTHOR] |
| Copyright of Ecology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 177074046 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Sparse modeling for climate variable selection across trophic levels. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Grames%2C+Eliza+M%2E%22">Grames, Eliza M.</searchLink><relatesTo>1,2</relatesTo><i> egrames@binghamton.edu</i><br /><searchLink fieldCode="AR" term="%22Forister%2C+Matthew+L%2E%22">Forister, Matthew L.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Ecology%22">Ecology</searchLink>. Mar2024, Vol. 105 Issue 3, p1-10. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Food+chains%22">Food chains</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+models%22">Atmospheric models</searchLink><br /><searchLink fieldCode="DE" term="%22Conservation+biology%22">Conservation biology</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+populations%22">Plant populations</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+fitness%22">Biological fitness</searchLink><br /><searchLink fieldCode="DE" term="%22Bird+populations%22">Bird populations</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+phenology%22">Plant phenology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Understanding how populations respond to climate is fundamentally important to many questions in ecology, evolution, and conservation biology. Climate is complex and multifaceted, with aspects affecting populations in different and sometimes unexpected ways. Thus, when measuring the changing climate it is important to consider the complexity of the phenomenon and the number of ways it can be characterized through different metrics. We used a Bayesian sparse modeling approach to select among 80 metrics of climate and applied the approach to 19 datasets of bird, insect, and plant population responses to abiotic conditions as case studies of how the method can be applied for climate variable selection in a time series context. For phenological datasets, mean spring temperature was frequently selected as an important climate driver, while selected predictors were more diverse for population metrics such as abundance or reproductive success. The climate variable selection approach presented here can help to identify potential climate metrics when there is limited physiological or mechanistic information to make an a priori variable selection, and is broadly applicable across studies on population responses to climate. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Ecology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/ecy.4231 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1 Subjects: – SubjectFull: Food chains Type: general – SubjectFull: Atmospheric models Type: general – SubjectFull: Conservation biology Type: general – SubjectFull: Plant populations Type: general – SubjectFull: Biological fitness Type: general – SubjectFull: Bird populations Type: general – SubjectFull: Plant phenology Type: general Titles: – TitleFull: Sparse modeling for climate variable selection across trophic levels. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Grames, Eliza M. – PersonEntity: Name: NameFull: Forister, Matthew L. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 00129658 Numbering: – Type: volume Value: 105 – Type: issue Value: 3 Titles: – TitleFull: Ecology Type: main |
| ResultId | 1 |
