The environment to the rescue: can physics help predict predator–prey interactions?

Understanding the factors that determine the occurrence and strength of ecological interactions under specific abiotic and biotic conditions is fundamental since many aspects of ecological community stability and ecosystem functioning depend on patterns of interactions among species. Current approaches to mapping food webs are mostly based on traits, expert knowledge, experiments, and/or statistical inference. However, they do not offer clear mechanisms explaining how trophic interactions are affected by the interplay between organism characteristics and aspects of the physical environment, such as temperature, light intensity or viscosity. Hence, they cannot yet predict accurately how local food webs will respond to anthropogenic pressures, notably to climate change and species invasions. Herein, we propose a framework that synthesises recent developments in food-web theory, integrating body size and metabolism with the physical properties of ecosystems. We advocate for combination of the movement paradigm with a modular definition of the predation sequence, because movement is central to predator–prey interactions, and a generic, modular model is needed to describe all the possible variation in predator–prey interactions. Pending sufficient empirical and theoretical knowledge, our framework will help predict the food-web impacts of well-studied physical factors, such as temperature and oxygen availability, as well as less commonly considered variables such as wind, turbidity or electrical conductivity. An improved predictive capability will facilitate a better understanding of ecosystem responses to a changing world.

---

Figure 1. The outcome of every predator–prey interaction is determined by a set of physical factors whose importance will depend on the properties of the predator, the prey, and the surrounding environment. (A) Sharks that possess the capacity to sense changes in small electromagnetic fields detect their prey through the electromagnetic field they produce. This behaviour is important in environments where low light levels reduce the effectiveness of vision (Whitehead & Collin, 2004). (B) The depth and density of snow determines its resistance to weight. This can provide an advantage to wolverines attacking heavier prey such as reindeer (Mattisson et al., 2016) because the lighter wolverine experiences less friction from the snow (Glass et al., 2021). Snow cover increases light reflection, which can also affect the outcome of predation (Griffin et al., 2005). (C) The ability of geckos to capture insects at night depends on the geometric complexity of their hunting ground, driving competition between native and invasive island geckos (Petren & Case, 1998). Luminosity (e.g. artificial lighting) is another driving factor, as well as surface roughness, which governs adhesion of the lizards to the solid structures over which they hunt (Persson, 2007). (D) Lions in search of prey tend to move crosswind over longer distances as wind speed increases in order to maximise odour detection probability (Wijers et al., 2022). This response to wind, however, is weakened by more intense moonlight when the lion reduces its reliance on olfaction and increases its use of vision for hunting. (E) Tropical hummingbirds can inhabit a wide altitudinal range. As altitude increases, oxygen partial pressure decreases, forcing the birds to reduce wingbeat frequency due to aerobic limitation of metabolic rate. Since air density decreases as well, the only option for the birds to keep flying is to increase their stroke amplitude (Altshuler & Dudley, 2003). The resulting additional energetic requirements affect their use of nectar resources with altitude (Hainsworth & Wolf, 1972). (F) Bacterial predators, such as Bdellovibrio sp., feed on other bacteria. Medium viscosity affects their swimming trajectories when searching for prey (Sathyamoorthy et al., 2019) and the drag forces acting on them, decreasing predation rate (Duncan et al., 2018). By affecting the same parameters, geometric complexity (e.g. granulometry; Dattner et al., 2017) also decreases Bdellovibirio sp. predation efficiency.

---

Published in Biological Reviews, 10 June 2024

Authors: Mehdi Cherif, Ulrich Brose, Myriam R. Hirt, Remo Ryser, Violette Silve, Georg Albert, Russell Arnott, Emilio Berti, Alyssa Cirtwill, Alexander Dyer, Benoit Gauzens, Anhubav Gupta, Hsi-Cheng Ho, Sébastien M. J. Portalier, Danielle Wain, Kate Wootton

Full text: https://doi.org/10.1111/brv.13105