What drives interaction strengths in complex food webs? A test with feeding rates of a generalist stream predator

Abstract

Describing the mechanisms that drive variation in species interaction strengths is central to understanding, predicting, and managing community dynamics. Multiple factors have been linked to trophic interaction strength variation, including species densities, species traits, and abiotic factors. Yet most empirical tests of the relative roles of multiple mechanisms that drive variation have been limited to simplified experiments that may diverge from the dynamics of natural food webs. Here, we used a field-based observational approach to quantify the roles of prey density, predator density, predator-prey body-mass ratios, prey identity, and abiotic factors in driving variation in feeding rates of reticulate sculpin (Cottus perplexus). We combined data on over 6,000 predator-prey observations with prey identification time functions to estimate 289 prey-specific feeding rates at nine stream sites in Oregon. Feeding rates on 57 prey types showed an approximately log-normal distribution, with few strong and many weak interactions. Model selection indicated that prey density, followed by prey identity, were the two most important predictors of prey-specific sculpin feeding rates. Feeding rates showed a positive relationship with prey taxon densities that was inconsistent with predator saturation predicted by current functional response models. Feeding rates also exhibited four orders-of-magnitude in variation across prey taxonomic orders, with the lowest feeding rates observed on prey with significant anti-predator defenses. Body-mass ratios were the third most important predictor variable, showing a hump-shaped relationship with the highest feeding rates at intermediate ratios. Sculpin density was negatively correlated with feeding rates, consistent with the presence of intraspecific predator interference. Our results highlight how multiple co-occurring drivers shape trophic interactions in nature and underscore ways in which simplified experiments or reliance on scaling laws alone may lead to biased inferences about the structure and dynamics of species-rich food webs.

Temporal shifts in intraspecific and interspecific diet variation: effects of predator body size and identity across seasons in a stream community

“…Despite aquatic snails ( Juga sp.) being the most abundant benthic macroinvertebrates by biomass in our study streams (Preston et al 2018b, Hawkins and Furnish 1987), sculpin feeding rates on snails were among the lowest for all observed prey, likely reflecting their low digestibility (Preston et al 2018b) and morphological constraints of predators (e.g., gape width and size of digestive tract). Additionally, cannibalism and intraguild predation among our focal predators (i.e., sculpin eating sculpin, trout eating trout, salamanders eating sculpin) was more common in larger individuals.…”

“…Total lengths of whole, intact prey items were measured to the nearest 0.5 mm. Additional details about study sites and data collection may be obtained in Preston et al (2018a) and Preston et al (2018b).…”

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).…”

Estimating predator functional responses using the times between prey captures

“…For example, Kalinkat et al (2013) examined the effects of predator and prey body sizes on the functional response across 25 different predator species feeding on eight differently sized prey species requiring 2,564 experimental units. Finally, foraging rates depend on both abiotic and biotic conditions (Abrams & Ginzburg, 2000;Gilbert et al, 2014;Preston et al, 2018;DeLong & Lyon, 2020), so identifying the way in which temperature, predator density, or habitat complexity, for example, influence the functional response generates the same level of replication challenge.…”