Reproductive Viability Analysis (RVA) as a new tool for ex situ population management
Hot off the presses!
Investigating factors that are driving reproductive success in ex situ animal populations has been a major part of my job since I joined the AZA Reproductive Management Center at the Saint Louis Zoo almost 2 years ago. The team has worked very hard on this project and we continue to work hard as this is ongoing work. In our recent publication “Reproductive Viability Analysis (RVA) as a new tool for ex situ population management” we compare four different modelling strategies for predicting reproductive success in captive animal populations, using the fennec fox (Vulpes zerda) and Mexican wolf (Canis lupus baileyi) Species Survival Plans® as our trial data sets. For me personally, it was my first dip into LASSO regression and Conditional Random Forest modeling. It was also a great opportunity to improve my proficiency coding in the R language. Check it out!
Reproductive Viability Analysis (RVA) as a new tool for ex situ population management
ABSTRACT
Many animal populations managed by the Association of Zoos and Aquariums’ (AZA) Species Survival Plans® (SSPs) have low rates of reproductive success. It is critical that individuals recommended to breed are successful to achieve genetic and demographic goals set by the SSP. Identifying factors that impact reproductive success can inform managers on best practices and improve demographic predictions. A Reproductive Viability Analysis (RVA) utilizes data gathered from Breeding and Transfer Plans, studbooks, and SSP documents, and through modeling identifies factors associated with reproductive success in a given species. Here, we describe the RVA process, including different statistical models with the highest accuracy for predicting reproductive success in fennec foxes (Vulpes zerda) and Mexican wolves (Canis lupus baileyi). Results from the RVA provide knowledge that can be used to make evidence‐based decisions about pairing and breeding strategies as well as improving reproductive success and population sustainability.
