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.