SANTOSTASI NINA LUISA
Nina Luisa Santostasi is a Ph.D. student in joint supervision between Sapienza University of Rome, Italy, and Centre d’Ecologie Fonctionnelle et Evolutive (affiliated with Université de Montpellier, France). She is working on modelling the demography of mammal populations in presence of hybridization, with a focus on two case studies: striped and common dolphins in the Gulf of Corinth, Greece, and wolf and dog in Italy. Her main interest is demographic modelling for the conservation and management of wildlife populations. In 2016 she was awarded a grant by Sapienza University of Rome, which allowed her to specialize at the Biostatistics and Population Biology Department at the Centre d’Ecologie Fonctionnelle et Evolutive. In 2014 she collaborated with the Marine Mammal Behavioral Ecology Group at Texas A&M University, organizing their photo-identification catalog in a digital database. For her M.Sc. thesis she relied on individual photo-identification and capture-recapture models to obtain estimates of dolphin population abundance in the Gulf of Corinth, Greece. In 2011 she was awarded a fellowship for studying abroad by Sapienza University of Rome that allowed her to study Statistics and Conservation Biology at North Carolina State University. Since 2011 she has been collaborating with Dolphin Biology and Conservation, working on dolphin population abundance estimates and optimal sampling strategies.
Modelling Population Dynamicsn In Presence Of HybridizationBackgroundAnthropogenic hybridization is a severe threat to biodiversity and raises increasing attention from scientists and conservationists. The phenomenon is increasing worldwide across plant and animal taxa and there is a strong need for developing new tools to evaluate and manage the risk.Hybridisation can be detected at the individual level using morphological or genetic data, and the raw percentage of hybrid individuals in the sample is often used as an estimate of prevalence in the population. However, when extrapolating this percentage to the whole population, we argue that prevalence should account for the issue of detectability less than one which is known to bias population abundance estimates. Besides, heterogeneity in the detection of hybrids and introgressed individuals is expected due to behavioural differences, which can cause even more bias in population abundance estimates.If the demographic parameters of a population are known (notably abundance, survival and reproductive rate) it is possible to project its abundance over time taking environmental and demographic stochasticity into account. When projecting the abundance of a population facing AH over time, its effects on demographic rates (e.g decreased reproduction) must be taken into account in the population projection to avoid the risk of underestimating its impacts on the population probability of persistence.The issue of imperfect and possibly heterogeneous detection can be addressed using capture-recapture protocols and models that allow the estimation of individual detectability and abundance of wildlife populations. In addition, population matrix models are widely used to project population abundance, quantify extinction probability and evaluate the effect of management actions on population growth rate. However, to the best of our knowledge, neither of these frameworks has ever been used in the context of animal populations facing AH.Objectives Of The Phd ProjectIn this project, I aim to:i) Develop new demographic tools to estimate prevalence of AH in free-ranging populations and predict the viability and fate of populations facing AH; (Chapter 1)ii) Illustrate the relevance of our methods by applying them on two different local AH case studies focusing on wolves and dolphins; (Chapter 2)iii) Use the developed models to estimate prevalence of AH and predict the viability and fate of populations facing AH on larger scales; (Chapter 3)