Measuring individual identity information in animal signals: Overview and performance of available identity metrics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027014%3A_____%2F19%3AN0000117" target="_blank" >RIV/00027014:_____/19:N0000117 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68081766:_____/19:00505878 RIV/60460709:41210/19:79544 RIV/60460709:41320/19:79544 RIV/60460709:41330/19:79544 a 2 dalších

Výsledek na webu

<a href="https://vuzv.cz/_privat/19116.pdf" target="_blank" >https://vuzv.cz/_privat/19116.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/2041-210X.13238" target="_blank" >10.1111/2041-210X.13238</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Measuring individual identity information in animal signals: Overview and performance of available identity metrics

Popis výsledku v původním jazyce

Identity signals have been studied for over 50 years but, and somewhat remarkably, there is no consensus as to how to quantify individuality in animal signals. While there is a variety of different metrics to quantify individuality, these methods remain un-validated and the relationships between them unclear. We contrasted three univariate and four multivariate identity metrics (and their different computational variants) and evaluated their performance on simulated and empirical datasets. Of the metrics examined, Beecher's information statistic (HS) performed closest to theoretical expectations and requirements for an ideal identity metric. It could be also easily and reliably converted into the commonly used discrimination score (and vice versa). Although Beecher's information statistic is not entirely independent of study sampling, this problem can be considerably lessened by reducing the number of parameters or by increasing the number of individuals in the analysis. Because it is easily calculated, has superior performance, can be used to quantify identity information in single variable or in a complete signal and because it indicates the number of individuals who can be discriminated given a set of measurements, we recommend that individuality should be quantified using Beecher's information statistic in future studies. Consistent use of Beecher's information statistic could enable meaningful comparisons and integration of results across different studies of individual identity signals. © 2019 The Authors. Methods in Ecology and Evolution

Název v anglickém jazyce

Measuring individual identity information in animal signals: Overview and performance of available identity metrics

Popis výsledku anglicky

Identity signals have been studied for over 50 years but, and somewhat remarkably, there is no consensus as to how to quantify individuality in animal signals. While there is a variety of different metrics to quantify individuality, these methods remain un-validated and the relationships between them unclear. We contrasted three univariate and four multivariate identity metrics (and their different computational variants) and evaluated their performance on simulated and empirical datasets. Of the metrics examined, Beecher's information statistic (HS) performed closest to theoretical expectations and requirements for an ideal identity metric. It could be also easily and reliably converted into the commonly used discrimination score (and vice versa). Although Beecher's information statistic is not entirely independent of study sampling, this problem can be considerably lessened by reducing the number of parameters or by increasing the number of individuals in the analysis. Because it is easily calculated, has superior performance, can be used to quantify identity information in single variable or in a complete signal and because it indicates the number of individuals who can be discriminated given a set of measurements, we recommend that individuality should be quantified using Beecher's information statistic in future studies. Consistent use of Beecher's information statistic could enable meaningful comparisons and integration of results across different studies of individual identity signals. © 2019 The Authors. Methods in Ecology and Evolution

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10614 - Behavioral sciences biology

Projekt

<a href="/cs/project/GA14-27925S" target="_blank" >GA14-27925S: Ontogenetická a sociální determinace hlasové individuality prasat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Methods in Ecology and Evolution

ISSN

2041-210X

e-ISSN

—

Svazek periodika

10

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

1558-1570

Kód UT WoS článku

000483699600017

EID výsledku v databázi Scopus

2-s2.0-85068530736