The landscape of fear: the missing link to understand top-down and bottom-up controls of prey abundance?

Identifying factors that may be responsible for 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), or predation (top-down), or interspecific competition (parallel). However, there are highly variable and often contradictory results regarding their relative strengths and influence. These varied results are often interpreted as indicating "shifting control" among the three main factors, or a complex, nonlinear relationship among environmental variables, resource availability, predation, and competition. We argue here that there is a "missing link" in our understanding of predator-prey dynamics. We explore whether the landscape-of-fear model can help us clarify the inconsistencies and increase our understanding of the roles, extent, and possible interactions of top-down, bottom-up, and parallel factors on prey population abundance. We propose two main predictions derived from the landscape-of-fear model: (1) 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 strong to weak as top-down impacts go from weak to strong; (2) for two or more species, interspecific competitive interactions produce various combinations of bottom-up, top-down, and parallel impacts depending on the dominant competing species and whether the landscapes of fear are shared or distinctive among competing species. We contend that these predictions could successfully explain many of the complex and contradictory results of current research. We test some of these predictions based on long-term data for small mammals from the Chihuahuan Desert in the United States, and Mexico. We conclude that the landscape-of-fear model does provide reasonable explanations for many of the reported studies and should be tested further to better understand the effects of bottom-up, top-down, and parallel factors on population dynamics.