Too Few, Too Many, or Just Right? Optimizing Sample Sizes for Population‐Level Inferences in Animal Tracking Projects.
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| Title: | Too Few, Too Many, or Just Right? Optimizing Sample Sizes for Population‐Level Inferences in Animal Tracking Projects. |
|---|---|
| Authors: | Silva, Inês1 (AUTHOR) i.simoes‐silva@hzdr.de, Fleming, Christen H.2,3 (AUTHOR), Noonan, Michael J.4,5,6 (AUTHOR), Fagan, William F.7 (AUTHOR), Calabrese, Justin M.1,7,8,9 (AUTHOR) |
| Source: | Ecology & Evolution (20457758). Jun2026, Vol. 16 Issue 6, p1-13. 13p. |
| Subject Terms: | *Conservation biology, Sample size (Statistics), Sampling methods, Mathematical statistics, Tracking & trailing, Inferential statistics, Home range (Animal geography) |
| Abstract: | Successful animal tracking projects depend on well‐informed sampling strategies and robust methods to yield biologically meaningful inferences. Considering financial and logistical constraints, the reliability of research outputs is shaped by key decisions regarding study duration (how long should each individual be tracked?), sampling frequency (how often should new locations be collected?), and how many individuals should be tracked. To maximize their conservation value, studies must consider estimator precision and avoid biased inferences of key parameters related to movement behavior and space use, as this can lead to wasted resources and misguide management actions. To address these challenges, we propose a workflow for determining the optimal sample sizes for population‐level home range area and speed estimates, explicitly addressing the trade‐offs between sampling duration (T$$ T $$), sampling interval (∆t$$ \Delta t $$), and population sample size (m$$ m $$). While a priori study design is considered best practice, this workflow can be applied at multiple stages, including concurrent with data collection, or as a post hoc evaluation. By selecting robust methods that are sampling‐insensitive, and by quantifying and propagating uncertainty through downstream analyses, we can determine whether our sample sizes (both at the individual‐ and population‐level) are sufficient to yield robust population‐level inferences. Furthermore, researchers can integrate additional logistical constraints such as fix success rate, location error, and potential device malfunctions, while also accounting for individual variation. We illustrate potential applications of this workflow through empirically‐guided simulations. To facilitate its use and implementation, we incorporated this workflow into the user‐friendly 'movedesign' R Shiny application. This application enables researchers to easily test different sampling strategies, and as of version 0.3.3, integrates population‐level analytical targets. This workflow has the potential to improve the rigor and reliability of animal tracking projects conducted under logistical and financial constraints, and thereby support more effective scientific research, wildlife management, and conservation efforts. [ABSTRACT FROM AUTHOR] |
| Copyright of Ecology & Evolution (20457758) 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: | GreenFILE |
| FullText | Text: Availability: 0 |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 194870677 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Too Few, Too Many, or Just Right? Optimizing Sample Sizes for Population‐Level Inferences in Animal Tracking Projects. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Silva%2C+Inês%22">Silva, Inês</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> i.simoes‐silva@hzdr.de</i><br /><searchLink fieldCode="AR" term="%22Fleming%2C+Christen+H%2E%22">Fleming, Christen H.</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Noonan%2C+Michael+J%2E%22">Noonan, Michael J.</searchLink><relatesTo>4,5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fagan%2C+William+F%2E%22">Fagan, William F.</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Calabrese%2C+Justin+M%2E%22">Calabrese, Justin M.</searchLink><relatesTo>1,7,8,9</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Ecology+%26+Evolution+%2820457758%29%22">Ecology & Evolution (20457758)</searchLink>. Jun2026, Vol. 16 Issue 6, p1-13. 13p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Conservation+biology%22">Conservation biology</searchLink><br /><searchLink fieldCode="DE" term="%22Sample+size+%28Statistics%29%22">Sample size (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Sampling+methods%22">Sampling methods</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+statistics%22">Mathematical statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Tracking+%26+trailing%22">Tracking & trailing</searchLink><br /><searchLink fieldCode="DE" term="%22Inferential+statistics%22">Inferential statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Home+range+%28Animal+geography%29%22">Home range (Animal geography)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Successful animal tracking projects depend on well‐informed sampling strategies and robust methods to yield biologically meaningful inferences. Considering financial and logistical constraints, the reliability of research outputs is shaped by key decisions regarding study duration (how long should each individual be tracked?), sampling frequency (how often should new locations be collected?), and how many individuals should be tracked. To maximize their conservation value, studies must consider estimator precision and avoid biased inferences of key parameters related to movement behavior and space use, as this can lead to wasted resources and misguide management actions. To address these challenges, we propose a workflow for determining the optimal sample sizes for population‐level home range area and speed estimates, explicitly addressing the trade‐offs between sampling duration (T$$ T $$), sampling interval (∆t$$ \Delta t $$), and population sample size (m$$ m $$). While a priori study design is considered best practice, this workflow can be applied at multiple stages, including concurrent with data collection, or as a post hoc evaluation. By selecting robust methods that are sampling‐insensitive, and by quantifying and propagating uncertainty through downstream analyses, we can determine whether our sample sizes (both at the individual‐ and population‐level) are sufficient to yield robust population‐level inferences. Furthermore, researchers can integrate additional logistical constraints such as fix success rate, location error, and potential device malfunctions, while also accounting for individual variation. We illustrate potential applications of this workflow through empirically‐guided simulations. To facilitate its use and implementation, we incorporated this workflow into the user‐friendly 'movedesign' R Shiny application. This application enables researchers to easily test different sampling strategies, and as of version 0.3.3, integrates population‐level analytical targets. This workflow has the potential to improve the rigor and reliability of animal tracking projects conducted under logistical and financial constraints, and thereby support more effective scientific research, wildlife management, and conservation efforts. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Ecology & Evolution (20457758) 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/ece3.73755 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Conservation biology Type: general – SubjectFull: Sample size (Statistics) Type: general – SubjectFull: Sampling methods Type: general – SubjectFull: Mathematical statistics Type: general – SubjectFull: Tracking & trailing Type: general – SubjectFull: Inferential statistics Type: general – SubjectFull: Home range (Animal geography) Type: general Titles: – TitleFull: Too Few, Too Many, or Just Right? Optimizing Sample Sizes for Population‐Level Inferences in Animal Tracking Projects. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Silva, Inês – PersonEntity: Name: NameFull: Fleming, Christen H. – PersonEntity: Name: NameFull: Noonan, Michael J. – PersonEntity: Name: NameFull: Fagan, William F. – PersonEntity: Name: NameFull: Calabrese, Justin M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20457758 Numbering: – Type: volume Value: 16 – Type: issue Value: 6 Titles: – TitleFull: Ecology & Evolution (20457758) Type: main |
| ResultId | 1 |
