Population viability analysis, based on population matrices is a widely adopted approach to predict the evolution of populations under different scenarios \citep{Boyce_1992,May_2019}. Integrated population modelling allows estimation of life history parameters and population numbers from different data sets simultaneously in the same model. This provides important benefits in terms of the number of parameters that can be estimated, as well as in the precision of the estimates \citep{Schaub_2010}. However,  one of the main obstacles when conducting a population viability assessment is the availability of detailed demographic data for the species of interest, which could compromise the quality of the assessment \citep{Brook_2000,Coulson_2001}.  We overcome the need of having detailed count data to estimate different life history parameters using presence/absence data collected during the South African Bird Atlas Project (SABAP2, \citealt{Brooks2020}) to fit a Bayesian dynamic occupancy model.  However, our main objective is not to estimate occupancy probabilities \citep[see][]{Royle_2007}, but to investigate changes in the population underlying occupancy, and how these changes relate to specific life history parameters \citep{Royle2003,Rossman_2016}. We allow the model to simultaneously estimate population size and life history parameters, but we use information published about the breeding ecology and satellite-tracked movements of the Black Harrier \citep{Curtis2004,Simmons2005,Garcia_Heras_2016,Garcia_Heras_2017,Garcia_Heras_2019} to define sensible priors for the model parameters. With a model for the population dynamics, undertake a population viability assessment for the species using Monte Carlo simulations to forecast scenarios under different levels of added mortality produced by wind farms.