Derivation of predator functional responses using a mechanistic approach in a natural system

Abstract

The functional response is central to our understanding of any predator-prey system as it establishes the link between trophic levels. Most functional responses are evaluated using phenomenological models linking predator acquisition rate and prey density. However, our ability to measure functional responses using such an approach is often limited in natural systems and the use of inaccurate functions can profoundly affect the outcomes of population and community models. Here, we develop a mechanistic model based on extensive data to assess the functional response of a generalist predator, the arctic fox (<|>Vulpes lagopus</|>), to various tundra prey species (lemmings and the nests of geese, passerines and sandpipers). We found that predator acquisition rates derived from the mechanistic model were consistent with field observations. Although sigmoidal functional responses were previously used to model fox-prey population dynamics, none of our simulations resulted in a saturating response in all prey species. Our results highlight the importance of predator searching components in predator-prey interactions, especially predator speed, while predator acquisition rates were not limited by handling processes. By combining theory with field observations, our study provides support that predator acquisition rate is not systematically limited at the highest prey densities observed in a natural system. We reinforce the idea that functional response categories, typically types I, II, and III, should be considered as particular cases along a continuum. Specific functions derived with a mechanistic approach for a range of densities observed in natural communities should improve our ability to model and understand predator-prey systems.

Quantifying the effects of intraspecific variation on predator feeding rates through nonlinear averaging

“…However, we note that even with increased attack-rate variation due to the inclusion of size variation, the effects of nonlinear averaging may not have increased substantially due to the weakly nonlinear relationships between attack and feeding rates (Figure 4a,c). In general, we therefore conclude that expectations on the strength of nonlinearaveraging effects must consider both how much trait variation is likely among individuals, and how nonlinear relationships between traits and interaction strengths are likely to be under conditions organisms experience in the field (see also Beardsell et al, 2021;Novak, 2010;Preston et al, 2018;Wootton & Emmerson, 2005).…”