Inferring the age of breeders from easily measurable variables

Este artículo contiene 9 páginas, 2 tablas, 2 figuras.
Age drives diferences in ftness components typically due to lower performances of younger and senescent individuals, and changes in breeding age structure infuence population dynamics and persistence. However, determining age and age structure is challenging in most species, where distinctive age features are lacking and available methods require substantial eforts or invasive procedures. Here we explore the potential to assess the age of breeders, or at least to identify young and senescent individuals, by measuring some breeding parameters partially driven by age (e.g. egg volume in birds). Taking advantage of a long-term population monitored seabird, we frst assessed whether age infuenced egg volume, and identifed other factors driving this trait by using general linear models. Secondly, we developed and evaluated a machine learning algorithm to assess the age of breeders using measurable variables. We confrmed that both younger and older individuals performed worse (less and smaller eggs) than middle-aged individuals. Our ensemble training algorithm was only able to distinguish young individuals, but not senescent breeders. We propose to test the combined use of feld monitoring, classic regression analysis and machine learning methods in other wild populations were measurable breeding parameters are partially driven by age, as a possible tool for assessing age structure in the wild.
Funding came partially from the Spanish Ministry of Science (CGL2017-85210) (MICINN/FEDER, UE), MG was partially supported by the European Union (MINOUW Project, H2020-634495) and KK was partially supported by the Erasmus+ programme of the European Union.