Scale Dependence of Predator–Prey Mass Ratio: Determinants and Applications

Abstract

Abstract Body size exerts a critical influence on predator–prey interactions and is therefore crucial for understanding the structure and dynamics of food webs. Currently, predator–prey mass ratio (PPMR) is regarded as the most promising modelling parameter for capturing the complex patterns of feeding links among species and individuals in a simplified way. While PPMR has been widely used in food-web modelling, its empirical estimation is more difficult, with the methodology remaining controversial. This is because PPMR (i) may be defined at different biological scales, such as from individuals to communities, and (ii) may also vary with biological factors, such as species identity and body mass, both of which conflict with the conventional model assumptions. In this chapter, we analyse recently compiled gut content data of marine food webs to address the two fundamental issues of scale-dependence and determinants of PPMR. We consider four definitions of PPMR: (i) species-averaged PPMR, (ii) link-averaged PPMR, (iii) individual-predator PPMR, and (iv) individual-link PPMR. First, we show that PPMR values have a complicated scale-dependence characterised by data elements, such as body mass and sample counts of predators and prey, due to averaging and sampling effects. We subsequently used AIC to systematically evaluate how the four types of PPMR are related to predator species identity and body mass. The results indicate that the model providing the best explanation for individual-predator and individual-link PPMRs incorporates both species identity and body mass. Meanwhile, the best model for species-averaged and link-averaged PPMRs was unclear, with different models being selected across sampling sites. These results imply that the size-based community-spectrum models describing individual-level interactions should include taxonomic dissimilarities. Based on the present study, we suggest that future research regarding PPMR must account for scale dependence and associated determinants to improve its utility as a widely applicable tool.