Identifying factors that may be responsible for affecting and possibly regulating the size of animal populations is a cornerstone in understanding population ecology.  The main factors that are thought to influence population size are either resources (bottom-up), predation, (top-down), or interspecific competition (called here- parallel influences).  However, studies of these effects have produced highly variable and often contradictory results regarding the relative strengths and influence of these factors. These varied results are often interpreted as indicating “shifting control” among the three forces or existence of a complex, non-linear relationship among precipitation, resource availability, predation, and competition.  We argue here that these interpretations suggest a “missing link” in our understanding of predator-prey dynamics.  We explore whether the landscape of fear model can clarify the inconsistencies and help us understand the roles, extent, and possible interactions of top-down, bottom-up, and parallel factors on prey population abundance.  We propose various predictions derived from the landscape of fear model.  For a single species, we suggest that as the makeup of the landscape of fear changes from relatively safe to relatively risky, bottom up impacts switch from comparatively strong to weak as top-down impacts go from weak to strong.  For two or more species, interspecific competitive interactions produce various combinations of bottom-up, top-down, and parallel impacts depending on which competing species is superior and whether their landscapes of fear are shared or distinctive.  We contend that these predications successfully explain many of the complex and contradictory results of current research.  We end by testing some of these predictions based on long-term data for small mammals from the Chihuahuan Desert in the U.S. and México.  We conclude that the landscape of fear model does provide reasonable explanations for many of the results found and should be tested further and that this model has the possibility of being the “missing link” in understanding bottom-up, top-down and parallel effects on population dynamics.