The Landscape of Fear: Ecological Implications of Being Afraid

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.

Foraging in the Landscape of Fear, the Predator’s Dilemma: Where Should I Hunt?

Female pumas (Puma concolor) give birth in all months of the year with a possible birth pulse in July–September. This pulse is proposed to be timed t

Estimates of predation rates by large predators can provide valuable information on their potential impact on their ungulate prey populations. This is especially the case for pumas Puma concolor and its main prey, mule deer Odocoileus hemionus. However, only limited information on predation rates of pumas exist where mule deer are the only ungulate prey available. I used VHF telemetry data collected over 24‐h monitoring sessions and once daily over consecutive days to derive two independent estimates of puma predation rates on mule deer where they were the only large prey available. For the 24‐h data, I had 48 time blocks on female pumas with kittens, 43 blocks on females without kittens and 30 blocks on males. For the daily consecutive data, the average number of consecutive days followed was 51.5±4.2 days. There were data on five female pumas with kittens, five pregnant females and nine females without kittens. Predation rates over an average month of 30 days from the 24‐h monitoring sessions were 2.0 mule deer per puma month for males (15.1 days per kill), 2.1 mule deer per puma month (14.3 days per kill) for females without kittens and 2.5 mule deer per puma month (12.0 days per kill) for pregnant females and females with kittens. For the consecutive daily data, females without kittens had an estimated predation rate of 2.1±0.14 mule deer per puma month (14.9±0.90 days per kill). Pregnant and females with kittens had predation rates of 2.7±0.18 and 2.6±0.21 mule deer per puma month, respectively (11.4±0.72 and 12.0±1.1 days per kill, respectively). Predation rates estimated in this study compared with those estimated by energetic demand for pumas in the study area but were lower than other field derived estimates. These data help increase our understanding of predation impacts of large predators on their prey.