Step-selection function (SSF) quantifies the environmental influences of animal movement and is important in identifying conservation strategies associated with landscape connectivity. SSF considers the step actually selected between two observed locations against a set of available steps which could have been taken by the individual. Conditional logistic regression is used to fit a set of parameters which can be used to determine the probability of habitat use. This method is increasingly being applied to biogeographical questions, but to-date no analysis on how the decisions made in the model building phase has been undertaken to address the uncertainty in the results. The number of alternative observations used in the regression, the method of generating alternative observations within a realistic location of the previous observation (e.g. using the empirical distribution of step length and turn angle of other individuals v space-time prisms), and the temporal resolution of the step are some of the factors that could influence the results. SSFs will be developed for 12 brown hyenas from Botswana and 6 oilbirds from Venezuela, with the analysis undertaken within Geospatial Modeling Environment and R 3.0.2. This study will provide researchers using the SSF framework to make decisions about how their models could be built. By understanding how the uncertainty in the output of an SSF can be apportioned to the different uncertainties in the inputs, any decisions made on the output can be made with more conviction.