Extracting reproductive parameters from GPS tracking data -- a new tool for a nesting raptor in Europe Article Swipe
Steffen Oppel
,
Ursin Beeli
,
Martin U. Grüebler
,
Valentijn S. van Bergen
,
Martin Kolbe
,
Thomas Pfeiffer
,
Patrick Scherler
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22541/au.169871345.53144870/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22541/au.169871345.53144870/v1
Understanding population dynamics requires estimation of demographic parameters like mortality and productivity. Because obtaining the necessary data for such parameters can be labour-intensive in the field, alternative approaches that estimate demographic parameters from existing data can be useful. High-resolution biologging data are now frequently available for large-bodied bird species, and can be used to estimate survival and productivity. We build on existing approaches to develop a new tool (‘NestTool’) that uses GPS tracking data at hourly resolution to estimate important productivity parameters such as territory acquisition, breeding propensity and breeding success. We developed NestTool with data from 258 individual red kites (Milvus milvus) from Switzerland tracked for up to 7 years. NestTool first extracts 42 movement metrics such as time within a user-specified radius, number of revisits, home range size, and distances between most frequently used day and night locations from the raw tracking data for each individual breeding season. These variables are then used in three successive random forest models to predict whether individuals exhibited home range behaviour, initiated a nesting attempt, and successfully raised fledglings. The models achieved > 95% accurate classification of home range and nesting behaviour in cross-validation data, but slightly lower (> 80%) accuracy in classifying the outcome of nesting attempts, because some individuals frequently returned to nests despite having failed. NestTool provides a graphical user interface to manually annotate those individual seasons for which model predictions fall below a user-defined threshold of certainty. When applied to tracking data from different red kite populations in Germany, NestTool yielded accurate predictions with > 80% accuracy in all parameters. NestTool is available as R package at https://github.com/Vogelwarte/NestTool and we encourage ornithologists to adapt it for different populations and species. NestTool will facilitate the more widespread estimation of demographic parameters from tracking data to inform population assessments
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.22541/au.169871345.53144870/v1
- https://www.authorea.com/doi/pdf/10.22541/au.169871345.53144870
- OA Status
- gold
- Cited By
- 1
- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388100551Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22541/au.169871345.53144870/v1Digital Object Identifier
- Title
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Extracting reproductive parameters from GPS tracking data -- a new tool for a nesting raptor in EuropeWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-31Full publication date if available
- Authors
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Steffen Oppel, Ursin Beeli, Martin U. Grüebler, Valentijn S. van Bergen, Martin Kolbe, Thomas Pfeiffer, Patrick ScherlerList of authors in order
- Landing page
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https://doi.org/10.22541/au.169871345.53144870/v1Publisher landing page
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https://www.authorea.com/doi/pdf/10.22541/au.169871345.53144870Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.authorea.com/doi/pdf/10.22541/au.169871345.53144870Direct OA link when available
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Range (aeronautics), Global Positioning System, Productivity, Population, Computer science, Estimation, Geography, Seasonal breeder, Ecology, Demography, Biology, Engineering, Telecommunications, Aerospace engineering, Systems engineering, Sociology, Macroeconomics, EconomicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
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68Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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