Hitchhikers’ guide to analysing bird ringing data

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

“…It is also possible to recode values to NAs after importing (see Harnos et al 2015a) using the ifelse() function. Another possibility is to directly substitute given values to NA.…”

“…This paper is the third part of our bird ringing data analyses series (Harnos et al 2015a(Harnos et al , 2016a) in which we focus on exploring missing data structures. We will give a short description of problems caused by missing data, and how they can bias basic descriptive statistics and explain how to generate and read useful figures to explore missing data patterns.…”

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Abstract This paper is the third part of our bird ringing data analysis series (Harnos et al 2015a(Harnos et al , 2016a) in which we continue to focus on exploring data using the R statistical environment. Here, we give a short description of missing data patterns, then handling and visualization techniques that help simultaneously explore the data and the structure of missing values.

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“…Here, we give a short description of missing data patterns, then handling and visualization techniques that help simultaneously explore the data and the structure of missing values. We try to emphasize that missing data can seriously distort the results of the analyses, therefore the conclusions drawn.Keywords: missing data pattern, exploration of incomplete data Összefoglalás Sorozatunk harmadik részében folytatjuk a madárgyűrűzési adatok kezelését, elemzését az R statisztikai program használatával (Harnos et al 2015a(Harnos et al , 2016a. Röviden bemutatunk hiányzó adat mintázatokat, kezelésüket és azt, hogy hogyan lehet a meglévő adatokat és a hiányzó adatok struktúráját szimultán feltárni különböző ábrázolási módszerekkel.…”

“…Keywords: missing data pattern, exploration of incomplete data Összefoglalás Sorozatunk harmadik részében folytatjuk a madárgyűrűzési adatok kezelését, elemzését az R statisztikai program használatával (Harnos et al 2015a(Harnos et al , 2016a. Röviden bemutatunk hiányzó adat mintázatokat, kezelésüket és azt, hogy hogyan lehet a meglévő adatokat és a hiányzó adatok struktúráját szimultán feltárni különböző ábrázolási módszerekkel.…”