Abstract
1. Understanding the role of predation in shaping the dynamics of animal communities is a fundamental issue in ecological research. Nevertheless, the complex nature of predator–prey interactions often prevents researchers from modelling them explicitly.2. By using periodic Leslie–Usher matrices and a simulation approach together with parameters obtained from long-term field projects, we reconstructed the underlying mechanisms of predator–prey demographic interactions and compared the dynamics of the roe deer–red fox–Eurasian lynx–human harvest system with those of the moose–brown bear–gray wolf–human harvest system in the boreal forest ecosystem of the southern Scandinavian Peninsula.3. The functional relationship of both roe deer and moose λ to changes in predation rates from the four predators was remarkably different. Lynx had the strongest impact among the four predators, whereas predation rates by wolves, red foxes, or brown bears generated minor variations in prey population λ. Elasticity values of lynx, wolf, fox and bear predation rates were −0·157, −0·056, −0·031 and −0·006, respectively, but varied with both predator and prey densities.4. Differences in predation impact were only partially related to differences in kill or predation rates, but were rather a result of different distribution of predation events among prey age classes. Therefore, the age composition of killed individuals emerged as the main underlying factor determining the overall per capita impact of predation.5. Our results confirm the complex nature of predator–prey interactions in large terrestrial mammals, by showing that different carnivores preying on the same prey species can exert a dramatically different demographic impact, even in the same ecological context, as a direct consequence of their predation patterns. Similar applications of this analytical framework in other geographical and ecological contexts are needed, but a more general evaluation of the subject is also required, aimed to assess, on a broader systematic and ecological range, what specific traits of a carnivore are most related to its potential impact on prey species.
Highlights
Understanding the contribution of predators in shaping the structure of ecological communities is a central issue in ecology
By using periodic Leslie–Usher matrices and a simulation approach together with parameters obtained from long-term field projects, we reconstructed the underlying mechanisms of predator– prey demographic interactions and compared the dynamics of the roe deer–red fox–Eurasian lynx–human harvest system with those of the moose–brown bear–gray wolf–human harvest system in the boreal forest ecosystem of the southern Scandinavian Peninsula
Lynx had the strongest impact among the four predators, whereas predation rates by wolves, red foxes, or brown bears generated minor variations in prey population k
Summary
Understanding the contribution of predators in shaping the structure of ecological communities is a central issue in ecology. Long-term studies of wolf–moose interactions in North America have long been the benchmark for large mammal predator–prey studies (Messier & Crete 1985; Messier 1994; Eberhardt 1997; Peterson 1999; Hayes & Harestad 2000), data from several other systems have begun to accumulate (Gese & Grothe 1995; Molinari-Jobin et al 2002; Laundre , Hernandez & Clark 2006; Nilsen et al 2009a) Despite such a strong research effort, there is still no general agreement on the degree to which predation influences prey population growth rate (k), and especially on the mechanisms of such processes. Generalizations cannot be drawn when dealing with predator–prey interactions, as the potential a predator has to limit a given prey population is influenced by a variety of ecological factors, such as spatiotemporal variation in the availability of alternative prey species (Hebblewhite et al 2003; Cooley et al 2008), presence of other predators (Atwood, Gese & Kunkel 2007), predator–prey body size relationships (Sinclair, Mduma & Brashares 2003; Owen-Smith & Mills 2008), habitat heterogeneity (Gorini et al 2011) and climate
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