<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Bird ringing datasets constitute possibly the largest source of temporal and spatial information on vertebrate taxa available on the globe. Initially, the method was invented to understand avian migration patterns. However, data deriving from bird ringing has been used in an array of other disciplines including population monitoring, changes in demography, conservation management and to study the effects of climate change to name a few. Despite the widespread usage and importance, there are no guidelines available specifically describing the practice of data management, preparation and analyses of ringing datasets. Here, we present the first of a series of comprehensive tutorials that may help fill this gap. We describe in detail and through a real-life example the intricacies of data cleaning and how to create a data table ready for analyses from raw ringing data in the R software environment. Moreover, we created and present here the R package; <monospace>ringR</monospace>, designed to carry out various specific tasks and plots related to bird ringing data. Most methods described here can also be applied to a wide range of capture-recapture type data based on individual marking, regardless to taxa or research question.</p></abstract>

Bird ringing datasets constitute possibly the largest source of temporal and spatial information on vertebrate taxa available on the globe. Initially, the method was invented to understand avian migration patterns. However, data deriving from bird ringing has been used in an array of other disciplines including population monitoring, changes in demography, conservation management and to study the effects of climate change to name a few. Despite the widespread usage and importance, there are no guidelines available specifically describing the practice of data management, preparation and analyses of ringing datasets. Here, we present the first of a series of comprehensive tutorials that may help fill this gap. We describe in detail and through a real-life example the intricacies of data cleaning and how to create a data table ready for analyses from raw ringing data in the R software environment. Moreover, we created and present here the R package; ringR, designed to carry out various specific tasks and plots related to bird ringing data. Most methods described here can also be applied to a wide range of capture-recapture type data based on individual marking, regardless to taxa or research question.

Hitchhikers’ guide to analysing bird ringing data

Department of Biomathematics and Informatics, Szent István University, Faculty of Veterinary Science, 1078 Budapest, István utca 2., Hungary

Department of Zoology, Hungarian Natural History Museum, 1088 Budapest, Baross utca 13., Hungary

Department of Anatomy, Cell- and Developmental Biology, Eötvös Loránd University, 1117 Budapest, Pázmány Péter sétány 1/C, Hungary

Ornis Hungarica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Part 1: data cleaning, preparation and exploratory analyses

Bird ringing datasets constitute possibly the largest source of temporal and spatial information on vertebrate taxa available on the globe. Initially, the...

Hitchhikers’ guide to analysing bird ringing data

Online veröffentlicht: 13. Jan. 2016

Seitenbereich: 163 - 188

DOI: https://doi.org/10.1515/orhu-2015-0018

Schlüsselwörter
data cleaning, R statistical software, banding data, statistical analysis, mark-recapture, data management

© 2015 Andrea Harnos et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Hitchhikers’ guide to analysing bird ringing data

Online veröffentlicht: 13. Jan. 2016

Seitenbereich: 163 - 188

DOI: https://doi.org/10.1515/orhu-2015-0018

Schlüsselwörterdata cleaning, R statistical software, banding data, statistical analysis, mark-recapture, data management

© 2015 Andrea Harnos et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
data cleaning, R statistical software, banding data, statistical analysis, mark-recapture, data management